{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialize PySpark\n",
    "\n",
    "First, we use the findspark package to initialize PySpark."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PySpark initiated...\n"
     ]
    }
   ],
   "source": [
    "# Initialize PySpark\n",
    "APP_NAME = \"Debugging Prediction Problems\"\n",
    "\n",
    "# If there is no SparkSession, create the environment\n",
    "try:\n",
    "  sc and spark\n",
    "except NameError as e:\n",
    "  import findspark\n",
    "  findspark.init()\n",
    "  import pyspark\n",
    "  import pyspark.sql\n",
    "\n",
    "  sc = pyspark.SparkContext()\n",
    "  spark = pyspark.sql.SparkSession(sc).builder.appName(APP_NAME).getOrCreate()\n",
    "\n",
    "print(\"PySpark initiated...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello, World!\n",
    "\n",
    "Loading data, mapping it and collecting the records into RAM..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Russell Jurney', 'Relato', 'CEO'],\n",
       " ['Florian Liebert', 'Mesosphere', 'CEO'],\n",
       " ['Don Brown', 'Rocana', 'CIO'],\n",
       " ['Steve Jobs', 'Apple', 'CEO'],\n",
       " ['Donald Trump', 'The Trump Organization', 'CEO'],\n",
       " ['Russell Jurney', 'Data Syndrome', 'Principal Consultant']]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the text file using the SparkContext\n",
    "csv_lines = sc.textFile(\"../data/example.csv\")\n",
    "\n",
    "# Map the data to split the lines into a list\n",
    "data = csv_lines.map(lambda line: line.split(\",\"))\n",
    "\n",
    "# Collect the dataset into local RAM\n",
    "data.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Objects from CSV using `pyspark.RDD.map`\n",
    "\n",
    "Using a function with a map operation to create objects (dicts) as records..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Russell Jurney', 'company': 'Relato', 'title': 'CEO'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn the CSV lines into objects\n",
    "def csv_to_record(line):\n",
    "  parts = line.split(\",\")\n",
    "  record = {\n",
    "    \"name\": parts[0],\n",
    "    \"company\": parts[1],\n",
    "    \"title\": parts[2]\n",
    "  }\n",
    "  return record\n",
    "\n",
    "# Apply the function to every record\n",
    "records = csv_lines.map(csv_to_record)\n",
    "\n",
    "# Inspect the first item in the dataset\n",
    "records.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `pyspark.RDD.groupBy`\n",
    "\n",
    "Using the groupBy operator to count the number of jobs per person..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Florian Liebert', <pyspark.resultiterable.ResultIterable object at 0x7f8a6020f438>)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'name': 'Florian Liebert', 'job_count': 1},\n",
       " {'name': 'Russell Jurney', 'job_count': 2},\n",
       " {'name': 'Don Brown', 'job_count': 1},\n",
       " {'name': 'Steve Jobs', 'job_count': 1},\n",
       " {'name': 'Donald Trump', 'job_count': 1}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the records by the name of the person\n",
    "grouped_records = records.groupBy(lambda x: x[\"name\"])\n",
    "\n",
    "# Show the first group\n",
    "print(grouped_records.first())\n",
    "\n",
    "# Count the groups\n",
    "job_counts = grouped_records.map(\n",
    "  lambda x: {\n",
    "    \"name\": x[0],\n",
    "    \"job_count\": len(x[1])\n",
    "  }\n",
    ")\n",
    "\n",
    "job_counts.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Use `pyspark.RDD.groupBy` to group executives by job title, then prepare records with the job title and the count of the number of executives with that job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Map vs FlatMap\n",
    "\n",
    "We need to understand the difference between the map and flatmap operators. Map groups items per-record, while flatMap creates a single large group of items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Russell Jurney', 'Relato', 'CEO'],\n",
       " ['Florian Liebert', 'Mesosphere', 'CEO'],\n",
       " ['Don Brown', 'Rocana', 'CIO'],\n",
       " ['Steve Jobs', 'Apple', 'CEO'],\n",
       " ['Donald Trump', 'The Trump Organization', 'CEO'],\n",
       " ['Russell Jurney', 'Data Syndrome', 'Principal Consultant']]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute a relation of words by line\n",
    "words_by_line = csv_lines\\\n",
    "  .map(lambda line: line.split(\",\"))\n",
    "\n",
    "words_by_line.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Russell Jurney',\n",
       " 'Relato',\n",
       " 'CEO',\n",
       " 'Florian Liebert',\n",
       " 'Mesosphere',\n",
       " 'CEO',\n",
       " 'Don Brown',\n",
       " 'Rocana',\n",
       " 'CIO',\n",
       " 'Steve Jobs',\n",
       " 'Apple',\n",
       " 'CEO',\n",
       " 'Donald Trump',\n",
       " 'The Trump Organization',\n",
       " 'CEO',\n",
       " 'Russell Jurney',\n",
       " 'Data Syndrome',\n",
       " 'Principal Consultant']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute a relation of words\n",
    "flattened_words = csv_lines\\\n",
    "  .map(lambda line: line.split(\",\"))\\\n",
    "  .flatMap(lambda x: x)\n",
    "flattened_words.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14, 6, 3, 15, 10, 3, 9, 6, 3, 10, 5, 3, 12, 22, 3, 14, 13, 20]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths = flattened_words.map(lambda x: len(x))\n",
    "lengths.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lengths.sum() / lengths.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Rows\n",
    "\n",
    "We can create `pyspark.sql.Rows` out of python objects so we can create `pyspark.sql.DataFrames`. This is desirable because once we have `DataFrames` we can run Spark SQL on our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(company='Relato', name='Russell Jurney', title='CEO')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.sql import Row\n",
    "\n",
    "# Convert the CSV into a pyspark.sql.Row\n",
    "def csv_to_row(line):\n",
    "  parts = line.split(\",\")\n",
    "  row = Row(\n",
    "    name=parts[0],\n",
    "    company=parts[1],\n",
    "    title=parts[2]\n",
    "  )\n",
    "  return row\n",
    "\n",
    "# Apply the function to get rows in an RDD\n",
    "rows = csv_lines.map(csv_to_row)\n",
    "rows.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. First count the number of companies for each executive, then create a `pyspark.sql.Row` for this result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Row(name='Florian Liebert', total=1),\n",
       " Row(name='Russell Jurney', total=2),\n",
       " Row(name='Don Brown', total=1),\n",
       " Row(name='Steve Jobs', total=1),\n",
       " Row(name='Donald Trump', total=1)]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = csv_lines.map(lambda line: line.split(','))\n",
    "records.collect()\n",
    "\n",
    "groups = records.groupBy(lambda x: x[0])\n",
    "counts = groups.map(lambda x: (x[0], len(x[1])))\n",
    "new_rows = counts.map(lambda x: Row(name=x[0], total=x[1]))\n",
    "new_rows.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+-----+\n",
      "|           name|total|\n",
      "+---------------+-----+\n",
      "|Florian Liebert|    1|\n",
      "| Russell Jurney|    2|\n",
      "|      Don Brown|    1|\n",
      "|     Steve Jobs|    1|\n",
      "|   Donald Trump|    1|\n",
      "+---------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "new_rows.toDF().select(\"name\",\"total\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating `DataFrames` from `RDDs`\n",
    "\n",
    "Using the `RDD.toDF()` method to create a dataframe, registering the `DataFrame` as a temporary table with Spark SQL, and counting the jobs per person using Spark SQL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+---------------+--------------------+\n",
      "|             company|           name|               title|\n",
      "+--------------------+---------------+--------------------+\n",
      "|              Relato| Russell Jurney|                 CEO|\n",
      "|          Mesosphere|Florian Liebert|                 CEO|\n",
      "|              Rocana|      Don Brown|                 CIO|\n",
      "|               Apple|     Steve Jobs|                 CEO|\n",
      "|The Trump Organiz...|   Donald Trump|                 CEO|\n",
      "|       Data Syndrome| Russell Jurney|Principal Consultant|\n",
      "+--------------------+---------------+--------------------+\n",
      "\n",
      "+---------------+-----+\n",
      "|           name|total|\n",
      "+---------------+-----+\n",
      "|   Donald Trump|    1|\n",
      "|Florian Liebert|    1|\n",
      "|      Don Brown|    1|\n",
      "| Russell Jurney|    2|\n",
      "|     Steve Jobs|    1|\n",
      "+---------------+-----+\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'name': 'Donald Trump', 'total': 1},\n",
       " {'name': 'Florian Liebert', 'total': 1},\n",
       " {'name': 'Don Brown', 'total': 1},\n",
       " {'name': 'Russell Jurney', 'total': 2},\n",
       " {'name': 'Steve Jobs', 'total': 1}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to a pyspark.sql.DataFrame\n",
    "rows_df = rows.toDF()\n",
    "\n",
    "rows_df.show()\n",
    "\n",
    "# Register the DataFrame for Spark SQL\n",
    "rows_df.registerTempTable(\"executives\")\n",
    "\n",
    "# Generate a new DataFrame with SQL using the SparkSession\n",
    "job_counts = spark.sql(\"\"\"\n",
    "SELECT\n",
    "  name,\n",
    "  COUNT(*) AS total\n",
    "  FROM executives\n",
    "  GROUP BY name\n",
    "\"\"\")\n",
    "job_counts.show()\n",
    "\n",
    "# Go back to an RDD\n",
    "job_counts.rdd.map(lambda x: x.asDict()).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `SparkContext.parallelize()`\n",
    "\n",
    "The oppotiste of `pyspark.RDD.collect()` is `SparkContext.parallelize()`. Whereas `collect` pulls data from Spark's memory into local RAM, `parallelize` sends data from local memory to Spark's memory. \n",
    "\n",
    "You can access it like this: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_rdd = sc.parallelize([1,2,3,4,5])\n",
    "my_rdd.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Create your own RDD of `dict` elements with named fields using sc.parallelize. Make it at least 5 records long.\n",
    "2. Convert this RDD of `dicts` into an RDD of `pyspark.sql.Row` elements.\n",
    "3. Convert this RDD of `pyspark.sql.Rows` into a `pyspark.sql.DataFrame`.\n",
    "4. Run a SQL `GROUP BY`/`COUNT` on your new `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+--------------+\n",
      "|       interest|          name|\n",
      "+---------------+--------------+\n",
      "| Ancient Greece|Russell Jurney|\n",
      "|Virtual Reality|  Chris Jurney|\n",
      "|         Sports|   Bill Jurney|\n",
      "|       Wildlife|   Ruth Jurney|\n",
      "|         Sports|     Bob Smith|\n",
      "+---------------+--------------+\n",
      "\n",
      "+---------------+-----+\n",
      "|       interest|total|\n",
      "+---------------+-----+\n",
      "|Virtual Reality|    1|\n",
      "|         Sports|    2|\n",
      "|       Wildlife|    1|\n",
      "| Ancient Greece|    1|\n",
      "+---------------+-----+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "my_data = [\n",
    "    {\"name\": \"Russell Jurney\", \"interest\": \"Ancient Greece\"},\n",
    "    {\"name\": \"Chris Jurney\", \"interest\": \"Virtual Reality\"},\n",
    "    {\"name\": \"Bill Jurney\", \"interest\": \"Sports\"},\n",
    "    {\"name\": \"Ruth Jurney\", \"interest\": \"Wildlife\"},\n",
    "    {\"name\": \"Bob Smith\", \"interest\": \"Sports\"}\n",
    "]\n",
    "my_rdd = sc.parallelize(my_data)\n",
    "my_rows = my_rdd.map(lambda x: Row(name=x[\"name\"], interest=x[\"interest\"]))\n",
    "\n",
    "my_df = my_rows.toDF()\n",
    "my_df.show()\n",
    "\n",
    "my_df.registerTempTable(\"people\")\n",
    "spark.sql(\"\"\"SELECT interest, COUNT(*) as total FROM people GROUP BY interest\"\"\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating `RDDs` from `DataFrames`\n",
    "\n",
    "We can easily convert back from a `DataFrame` to an `RDD` using the `pyspark.sql.DataFrame.rdd()` method, along with `pyspark.sql.Row.asDict()` if we desire a Python `dict` of our records."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Donald Trump', 'total': 1},\n",
       " {'name': 'Florian Liebert', 'total': 1},\n",
       " {'name': 'Don Brown', 'total': 1},\n",
       " {'name': 'Russell Jurney', 'total': 2},\n",
       " {'name': 'Steve Jobs', 'total': 1}]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_counts.rdd.map(lambda x: x.asDict()).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises\n",
    "\n",
    "1. Using the data from item 4 in the exercise above, convert the data back to its original form, a local collection of `dict` elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'interest': 'Ancient Greece', 'name': 'Russell Jurney'},\n",
       " {'interest': 'Virtual Reality', 'name': 'Chris Jurney'},\n",
       " {'interest': 'Sports', 'name': 'Bill Jurney'},\n",
       " {'interest': 'Wildlife', 'name': 'Ruth Jurney'},\n",
       " {'interest': 'Sports', 'name': 'Bob Smith'}]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_df.rdd.map(lambda x: x.asDict()).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading and Inspecting Parquet Files\n",
    "\n",
    "Using the `SparkSession` to load files as `DataFrames` and inspecting their contents..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Quarter',\n",
       " 'Month',\n",
       " 'DayofMonth',\n",
       " 'DayOfWeek',\n",
       " 'FlightDate',\n",
       " 'Carrier',\n",
       " 'TailNum',\n",
       " 'FlightNum',\n",
       " 'Origin',\n",
       " 'OriginCityName',\n",
       " 'OriginState',\n",
       " 'Dest',\n",
       " 'DestCityName',\n",
       " 'DestState',\n",
       " 'DepTime',\n",
       " 'DepDelay',\n",
       " 'DepDelayMinutes',\n",
       " 'TaxiOut',\n",
       " 'TaxiIn',\n",
       " 'WheelsOff',\n",
       " 'WheelsOn',\n",
       " 'ArrTime',\n",
       " 'ArrDelay',\n",
       " 'ArrDelayMinutes',\n",
       " 'Cancelled',\n",
       " 'Diverted',\n",
       " 'ActualElapsedTime',\n",
       " 'AirTime',\n",
       " 'Flights',\n",
       " 'Distance',\n",
       " 'CarrierDelay',\n",
       " 'WeatherDelay',\n",
       " 'NASDelay',\n",
       " 'SecurityDelay',\n",
       " 'LateAircraftDelay',\n",
       " 'CRSDepTime',\n",
       " 'CRSArrTime']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the parquet file containing flight delay records\n",
    "on_time_dataframe = spark.read.parquet('../data/on_time_performance.parquet')\n",
    "\n",
    "# Register the data for Spark SQL\n",
    "on_time_dataframe.registerTempTable(\"on_time_performance\")\n",
    "\n",
    "# Check out the columns\n",
    "on_time_dataframe.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------+------+----+-------+--------+--------+\n",
      "|FlightDate|TailNum|Origin|Dest|Carrier|DepDelay|ArrDelay|\n",
      "+----------+-------+------+----+-------+--------+--------+\n",
      "|2015-01-01| N919DE|   IND| MSP|     DL|    -9.0|   -20.0|\n",
      "|2015-01-10| N28478|   IAH| PHL|     UA|    33.0|    22.0|\n",
      "|2015-01-10| N75410|   SLC| IAH|     UA|    30.0|    29.0|\n",
      "|2015-01-12| N548AS|   LAX| PDX|     AS|    -6.0|     3.0|\n",
      "|2015-01-12| N608SK|   MSP| FSD|     OO|    -3.0|   -12.0|\n",
      "|2015-01-12| N804SK|   LAX| OAK|     OO|    -5.0|    -7.0|\n",
      "|2015-01-12| N7707C|   OAK| PDX|     WN|    11.0|    13.0|\n",
      "|2015-01-14| N630MQ|   ORD| FAR|     MQ|    75.0|    62.0|\n",
      "|2015-01-15| N133EV|   HPN| ATL|     EV|    -4.0|     4.0|\n",
      "|2015-01-16| N530AS|   SEA| LAX|     AS|    10.0|     7.0|\n",
      "+----------+-------+------+----+-------+--------+--------+\n",
      "only showing top 10 rows\n",
      "\n",
      "+----------+-------+------+----+-------+--------+--------+\n",
      "|FlightDate|TailNum|Origin|Dest|Carrier|DepDelay|ArrDelay|\n",
      "+----------+-------+------+----+-------+--------+--------+\n",
      "|2015-01-01| N003AA|   HDN| DFW|     AA|   332.0|   336.0|\n",
      "|2015-01-01| N012AA|   MIA| TPA|     AA|    null|    null|\n",
      "|2015-01-01| N020AA|   IAD| MIA|     AA|    -6.0|     1.0|\n",
      "|2015-01-01| N023AA|   STL| MIA|     AA|   109.0|   106.0|\n",
      "|2015-01-01| N200AA|   DFW| DEN|     AA|    47.0|    40.0|\n",
      "|2015-01-01| N3AAAA|   RDU| MIA|     AA|    -8.0|    10.0|\n",
      "|2015-01-01| N3ACAA|   DFW| MIA|     AA|    21.0|    22.0|\n",
      "|2015-01-01| N3AKAA|   ORD| RSW|     AA|     5.0|    10.0|\n",
      "|2015-01-01| N3ALAA|   DFW| CLT|     AA|     8.0|    -9.0|\n",
      "|2015-01-01| N3AMAA|   LAX| IAD|     AA|    45.0|    39.0|\n",
      "+----------+-------+------+----+-------+--------+--------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Trim the fields and keep the result\n",
    "trimmed_on_time = on_time_dataframe\\\n",
    "  .select(\n",
    "    \"FlightDate\",\n",
    "    \"TailNum\",\n",
    "    \"Origin\",\n",
    "    \"Dest\",\n",
    "    \"Carrier\",\n",
    "    \"DepDelay\",\n",
    "    \"ArrDelay\"\n",
    "  )\n",
    "\n",
    "# Sample 0.01% of the data and show\n",
    "trimmed_on_time.sample(False, 0.0001).show(10)\n",
    "\n",
    "sampled_ten_percent = trimmed_on_time.sample(False, 0.1)\n",
    "sampled_ten_percent.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `DataFrame` Workflow: Calculating Speed in Dataflow and SQL\n",
    "\n",
    "We can go back and forth between dataflow programming and SQL programming using `pyspark.sql.DataFrames`. This enables us to get the best of both worlds from these two APIs. For example, if we want to group records and get a total count for each group... a SQL `SELECT/GROUP BY/COUNT` is the most direct way to do it. On the other hand, if we want to filter data, a dataflow API call like `DataFrame.filter()` is the cleanest way. This comes down to personal preference for the user. In time you will develop your own style of working.\n",
    "\n",
    "### Dataflow Programming\n",
    "\n",
    "If we were to look at the `AirTime` along with the `Distance`, we could get a good idea of how fast the airplanes were going. Pretty cool! Lets do this using Dataflows first.\n",
    "\n",
    "#### Trimming Our Data\n",
    "\n",
    "First lets select just the two columns of interest: `AirTime` and `Distance`. We can always go back and select more columns if we want to extend our analysis, but trimming uneeded fields optimizes performance right away."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+--------+\n",
      "|AirTime|Distance|\n",
      "+-------+--------+\n",
      "|   59.0|   432.0|\n",
      "|   77.0|   432.0|\n",
      "|  129.0|   802.0|\n",
      "|   93.0|   731.0|\n",
      "|  111.0|   769.0|\n",
      "|  108.0|   769.0|\n",
      "+-------+--------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fd = on_time_dataframe.select(\"AirTime\", \"Distance\")\n",
    "fd.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From Minutes to Hours\n",
    "\n",
    "Now lets convert our `AirTime` from minutes to hours by dividing by 60."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+--------+\n",
      "|             Hours|Distance|\n",
      "+------------------+--------+\n",
      "|0.9833333333333333|   432.0|\n",
      "|1.2833333333333334|   432.0|\n",
      "|              2.15|   802.0|\n",
      "|              1.55|   731.0|\n",
      "|              1.85|   769.0|\n",
      "+------------------+--------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "hourly_fd = fd.select((fd.AirTime / 60).alias('Hours'), \"Distance\")\n",
    "hourly_fd.show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Raw Calculation\n",
    "\n",
    "Now lets calculate miles per hour!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|               Mph|\n",
      "+------------------+\n",
      "| 439.3220338983051|\n",
      "| 336.6233766233766|\n",
      "| 373.0232558139535|\n",
      "|471.61290322580646|\n",
      "| 415.6756756756757|\n",
      "|427.22222222222223|\n",
      "| 430.2739726027398|\n",
      "|              null|\n",
      "|              null|\n",
      "|              null|\n",
      "+------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "miles_per_hour = hourly_fd.select(\n",
    "    (hourly_fd.Distance / hourly_fd.Hours).alias('Mph')\n",
    ")\n",
    "miles_per_hour.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Investigating `nulls`\n",
    "\n",
    "Looks like we have some errors in some records in our calculation because of missing fields? Lets bring back in the `Distance` and `AirTime` fields to see where the problem is coming from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------------+--------+\n",
      "|AirTime|             Hours|Distance|\n",
      "+-------+------------------+--------+\n",
      "|   59.0|0.9833333333333333|   432.0|\n",
      "|   77.0|1.2833333333333334|   432.0|\n",
      "|  129.0|              2.15|   802.0|\n",
      "|   93.0|              1.55|   731.0|\n",
      "|  111.0|              1.85|   769.0|\n",
      "|  108.0|               1.8|   769.0|\n",
      "|  146.0| 2.433333333333333|  1047.0|\n",
      "|   null|              null|  1007.0|\n",
      "|   null|              null|  1007.0|\n",
      "|   null|              null|   802.0|\n",
      "|   null|              null|   731.0|\n",
      "|  122.0| 2.033333333333333|   731.0|\n",
      "|   94.0|1.5666666666666667|   731.0|\n",
      "|   91.0|1.5166666666666666|   731.0|\n",
      "|  115.0|1.9166666666666667|   731.0|\n",
      "|   89.0|1.4833333333333334|   731.0|\n",
      "|  106.0|1.7666666666666666|   721.0|\n",
      "|   94.0|1.5666666666666667|   748.0|\n",
      "|   null|              null|   733.0|\n",
      "|   null|              null|   733.0|\n",
      "+-------+------------------+--------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fd.select(\n",
    "    \"AirTime\", \n",
    "    (fd.AirTime / 60).alias('Hours'), \n",
    "    \"Distance\"\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering `nulls`\n",
    "\n",
    "Now that we know some records are missing `AirTimes`, we can filter those records using `pyspark.sql.DataFrame.filter()`. Starting from the beginning, lets recalculate our values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|               Mph|\n",
      "+------------------+\n",
      "| 439.3220338983051|\n",
      "| 336.6233766233766|\n",
      "| 373.0232558139535|\n",
      "|471.61290322580646|\n",
      "| 415.6756756756757|\n",
      "|427.22222222222223|\n",
      "| 430.2739726027398|\n",
      "| 359.5081967213115|\n",
      "|466.59574468085106|\n",
      "|  481.978021978022|\n",
      "+------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fd = on_time_dataframe.select(\"AirTime\", \"Distance\")\n",
    "filled_fd = fd.filter(fd.AirTime.isNotNull())\n",
    "hourly_fd = filled_fd.select(\n",
    "    \"AirTime\", \n",
    "    (filled_fd.AirTime / 60).alias('Hours'), \n",
    "    \"Distance\"\n",
    ")\n",
    "mph = hourly_fd.select((hourly_fd.Distance / hourly_fd.Hours).alias('Mph'))\n",
    "mph.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Averaging Speed\n",
    "\n",
    "How fast does the fleet travel overall? Lets compute the average speed for the entire fleet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------------------+\n",
      "|          avg(Mph)|\n",
      "+------------------+\n",
      "|408.72370268242895|\n",
      "+------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from pyspark.sql.functions import avg\n",
    "\n",
    "mph.select(\n",
    "    pyspark.sql.functions.avg(mph.Mph)\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the average speed of the fleet is 408 mph. Note how along the way we chekced the data for sanity, which led to confidence in our answer. SQL by contast can hide the internals of a query, which might have skewed our average significantly! \n",
    "\n",
    "### SQL-Based Speed Calculation\n",
    "\n",
    "Now lets work the same thing out in SQL. Starting from the top:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+\n",
      "|              Mph|\n",
      "+-----------------+\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|265.7142857142857|\n",
      "|            232.5|\n",
      "|            232.5|\n",
      "|            232.5|\n",
      "+-----------------+\n",
      "only showing top 10 rows\n",
      "\n",
      "+-----------------+\n",
      "|         avg(Mph)|\n",
      "+-----------------+\n",
      "|408.7237026822692|\n",
      "+-----------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "on_time_dataframe.registerTempTable(\"on_time_performance\")\n",
    "\n",
    "mph = spark.sql(\"\"\"\n",
    "SELECT ( Distance / ( AirTime/60 ) ) AS Mph \n",
    "FROM on_time_performance \n",
    "WHERE AirTime IS NOT NULL\n",
    "ORDER BY AirTime\n",
    "\"\"\")\n",
    "mph.show(10)\n",
    "\n",
    "mph.registerTempTable(\"mph\")\n",
    "spark.sql(\"SELECT AVG(Mph) from mph\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluating SQL\n",
    "\n",
    "The SQL based solution seems to be better in this case, because we can simply express our calculation all in one place. When complexity grows however, it is best to break a single query into multiple stages where you use SQL or Dataflow programming to massage the data into shape."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating Histograms\n",
    "\n",
    "Having computed the speed in miles per hour and the overall average speed of passenger jets in the US, lets dig deeper by using the `RDD` API's histogram method to calculate histograms buckets and values, which will then use to visualize data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([32.34375,\n",
       "  107.89626024590164,\n",
       "  183.4487704918033,\n",
       "  259.0012807377049,\n",
       "  334.5537909836066,\n",
       "  410.10630122950823,\n",
       "  485.65881147540983,\n",
       "  561.2113217213115,\n",
       "  636.7638319672132,\n",
       "  712.3163422131148,\n",
       "  787.8688524590165],\n",
       " [174, 14255, 148314, 643901, 1861699, 2357002, 664609, 23997, 32, 25])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute a histogram of departure delays\n",
    "mph\\\n",
    "  .select(\"Mph\")\\\n",
    "  .rdd\\\n",
    "  .flatMap(lambda x: x)\\\n",
    "  .histogram(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing Histograms\n",
    "\n",
    "The problem with the output above is that it is hard to interpret. For better understanding, we need a visualization. We can use `matplotlib` inline in a Jupyter Notebook to visualize this distribution and see what the tendency of speed of airplanes is around the mean of 408 mph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to plot a histogram using pyplot\n",
    "def create_hist(rdd_histogram_data):\n",
    "  \"\"\"Given an RDD.histogram, plot a pyplot histogram\"\"\"\n",
    "  heights = np.array(rdd_histogram_data[1])\n",
    "  full_bins = rdd_histogram_data[0]\n",
    "  mid_point_bins = full_bins[:-1]\n",
    "  widths = [abs(i - j) for i, j in zip(full_bins[:-1], full_bins[1:])]\n",
    "  bar = plt.bar(mid_point_bins, heights, width=widths, color='b')\n",
    "  return bar\n",
    "\n",
    "# Compute a histogram of departure delays\n",
    "departure_delay_histogram = mph\\\n",
    "  .select(\"Mph\")\\\n",
    "  .rdd\\\n",
    "  .flatMap(lambda x: x)\\\n",
    "  .histogram(10)\n",
    "\n",
    "create_hist(departure_delay_histogram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating on a Histogram\n",
    "\n",
    "That looks interesting, but the bars seem to fat the really see what is going on. Lets double the number of buckets from 10 to 20. We can reuse the `create_hist()` method to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute a histogram of departure delays\n",
    "departure_delay_histogram = mph\\\n",
    "  .select(\"Mph\")\\\n",
    "  .rdd\\\n",
    "  .flatMap(lambda x: x)\\\n",
    "  .histogram(20)\n",
    "\n",
    "create_hist(departure_delay_histogram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Speed Summary\n",
    "\n",
    "You've now seen how to calculate different values in both SQL and Dataflow style, how to switch between the two methods, how to switch between the `pyspark.RDD` and `pyspark.sql.DataFrame` APIs and you're starting to build a proficiency in PySpark!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counting Airplanes in the US Fleet\n",
    "\n",
    "Lets convert our `on_time_dataframe` (a `DataFrame`) into an `RDD` to calculate the total number of airplanes in the US fleet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total airplanes: 4897\n"
     ]
    }
   ],
   "source": [
    "# Dump the unneeded fields\n",
    "tail_numbers = on_time_dataframe.rdd.map(lambda x: x.TailNum)\n",
    "tail_numbers = tail_numbers.filter(lambda x: x != '' and x is not None)\n",
    "\n",
    "# distinct() gets us unique tail numbers\n",
    "unique_tail_numbers = tail_numbers.distinct()\n",
    "\n",
    "# now we need a count() of unique tail numbers\n",
    "airplane_count = unique_tail_numbers.count()\n",
    "print(\"Total airplanes: {}\".format(airplane_count))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 1: Characterizing Airports\n",
    "\n",
    "Using the techniques we demonstated above, calculate any 3 out of 4 of the following things using both the SQL and the Dataflow methods for each one. That is: implement each calculation twice - once in SQL and once using Dataflows. Try to use both the `RDD` and `DataFrame` APIs as you work.\n",
    "\n",
    "1. How many airports are there in the united states?\n",
    "2. What is the average flight time for flights arriving in San Francisco (SFO)? What does the distribution of this value look like? Plot a histogram using the `create_hist` method shown above.\n",
    "3. Which American airport has the fastest out-bound speeds? What does the distribution of the flight speeds at this one airport look like? Plot a histogram using the `create_hist` method shown above.\n",
    "4. What were the worst travel dates in terms of overall delayed flights in the US in 2015?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+------------------+\n",
      "|Origin|          avg(Mph)|\n",
      "+------+------------------+\n",
      "|   BGM| 346.1853538898441|\n",
      "|   PSE| 452.6117680000053|\n",
      "|   INL|286.26851316932107|\n",
      "|   DLG|395.85893026325215|\n",
      "|   MSY| 417.8806617127785|\n",
      "|   PPG| 504.2422916767315|\n",
      "|   GEG|  402.285589290541|\n",
      "|   SNA| 417.1687699400066|\n",
      "|   BUR|381.54113589717645|\n",
      "|   GRB| 304.8341806425415|\n",
      "|   GTF| 425.3390308053375|\n",
      "|   IDA|352.21033421102914|\n",
      "|   GRR| 345.7402330340281|\n",
      "|   JLN|333.89806917791054|\n",
      "|   PSG| 218.6399366568302|\n",
      "|   EUG|408.36973646170964|\n",
      "|   PVD|  377.284933612292|\n",
      "|   GSO| 363.1599876379004|\n",
      "|   MYR|392.41545245530034|\n",
      "|   OAK| 411.1720814918298|\n",
      "+------+------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "origin_hour_dist = on_time_dataframe.filter(\n",
    "    on_time_dataframe.AirTime.isNotNull()\n",
    ").select(\n",
    "    \"Origin\", \n",
    "    (on_time_dataframe.AirTime/60).alias(\"Hours\"), \n",
    "    \"Distance\"\n",
    ")\n",
    "mph_origins = origin_hour_dist.select(\n",
    "    \"Origin\", \n",
    "    (origin_hour_dist.Distance / origin_hour_dist.Hours).alias(\"Mph\")\n",
    ")\n",
    "mph_origins.registerTempTable(\"mph_origins\")\n",
    "\n",
    "avg_speeds = mph_origins.groupBy(\"Origin\").agg({\"Mph\": \"avg\"}).alias(\"Mph\")\n",
    "avg_speeds.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "on_time_dataframe.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating with `DataFrame.groupBy`\n",
    "\n",
    "We can use Spark SQL to calculate things using `DataFrames`, but we can also group data and calculate as we did with `RDDs`. For a full list of methods you can apply to grouped `DataFrames`, see [the documentation for pyspark.sql.GroupedData](http://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.GroupedData). Below we will demonstrate some of these methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----------------+--------------------+------------------+---------------+-----------------+--------------------+--------------------+-------------+----------------------+-----------------+------------+-------------+-----------------+-----------------+------------------+--------------------+----------------------+\n",
      "|Origin|   avg(DepDelay)|avg(DepDelayMinutes)|      avg(TaxiOut)|    avg(TaxiIn)|    avg(ArrDelay)|avg(ArrDelayMinutes)|      avg(Cancelled)|avg(Diverted)|avg(ActualElapsedTime)|     avg(AirTime)|avg(Flights)|avg(Distance)|avg(CarrierDelay)|avg(WeatherDelay)|     avg(NASDelay)|  avg(SecurityDelay)|avg(LateAircraftDelay)|\n",
      "+------+----------------+--------------------+------------------+---------------+-----------------+--------------------+--------------------+-------------+----------------------+-----------------+------------+-------------+-----------------+-----------------+------------------+--------------------+----------------------+\n",
      "|   BGM|9.42056074766355|  15.177570093457945|16.193146417445483|6.2398753894081|6.218068535825545|   16.32398753894081|0.009259259259259259|          0.0|      88.4423676012461|66.00934579439253|         1.0|        378.0|58.67307692307692|              0.0|10.711538461538462|0.057692307692307696|    26.903846153846153|\n",
      "+------+----------------+--------------------+------------------+---------------+-----------------+--------------------+--------------------+-------------+----------------------+-----------------+------------+-------------+-----------------+-----------------+------------------+--------------------+----------------------+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+-----------------+\n",
      "|Origin|     avg(AirTime)|\n",
      "+------+-----------------+\n",
      "|   BGM|66.00934579439253|\n",
      "+------+-----------------+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+-----+\n",
      "|Origin|count|\n",
      "+------+-----+\n",
      "|   BGM|  324|\n",
      "+------+-----+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+------------+\n",
      "|Origin|max(AirTime)|\n",
      "+------+------------+\n",
      "|   BGM|        91.0|\n",
      "+------+------------+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+-------------+--------------------+------------+-----------+-------------+--------------------+--------------+-------------+----------------------+------------+------------+-------------+-----------------+-----------------+-------------+------------------+----------------------+\n",
      "|Origin|max(DepDelay)|max(DepDelayMinutes)|max(TaxiOut)|max(TaxiIn)|max(ArrDelay)|max(ArrDelayMinutes)|max(Cancelled)|max(Diverted)|max(ActualElapsedTime)|max(AirTime)|max(Flights)|max(Distance)|max(CarrierDelay)|max(WeatherDelay)|max(NASDelay)|max(SecurityDelay)|max(LateAircraftDelay)|\n",
      "+------+-------------+--------------------+------------+-----------+-------------+--------------------+--------------+-------------+----------------------+------------+------------+-------------+-----------------+-----------------+-------------+------------------+----------------------+\n",
      "|   BGM|        993.0|                 993|       100.0|       30.0|       1005.0|              1005.0|             1|            0|                 163.0|        91.0|           1|        378.0|            993.0|              0.0|         74.0|               3.0|                 364.0|\n",
      "+------+-------------+--------------------+------------+-----------+-------------+--------------------+--------------+-------------+----------------------+------------+------------+-------------+-----------------+-----------------+-------------+------------------+----------------------+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+------------+\n",
      "|Origin|min(AirTime)|\n",
      "+------+------------+\n",
      "|   BGM|        56.0|\n",
      "+------+------------+\n",
      "only showing top 1 row\n",
      "\n",
      "+------+------------+\n",
      "|Origin|sum(AirTime)|\n",
      "+------+------------+\n",
      "|   BGM|     21189.0|\n",
      "+------+------------+\n",
      "only showing top 1 row\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate average of every numeric field\n",
    "on_time_dataframe.groupBy(\"Origin\").avg().show(1)\n",
    "\n",
    "# Calculate verage AirTime per origin city\n",
    "on_time_dataframe.groupBy(\"Origin\").agg({\"AirTime\": \"mean\"}).show(1)\n",
    "\n",
    "# Get the count of flights from each origin\n",
    "on_time_dataframe.groupBy(\"Origin\").count().show(1)\n",
    "\n",
    "# Get the maximum airtime for flights leaving each city\n",
    "on_time_dataframe.groupby(\"Origin\").agg({\"AirTime\": \"max\"}).show(1)\n",
    "\n",
    "# Get the maximum of all numeric columns for flights leaving each city\n",
    "on_time_dataframe.groupBy(\"Origin\").max().show(1)\n",
    "\n",
    "# Get the shortest flight for each origin airport\n",
    "on_time_dataframe.groupBy(\"Origin\").agg({\"AirTime\": \"min\"}).show(1)\n",
    "\n",
    "# Total minutes flown from each airport\n",
    "on_time_dataframe.groupBy(\"Origin\").agg({\"AirTime\": \"sum\"}).show(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivoting DataFrames\n",
    "\n",
    "One useful function of `DataFrames` is `pivot`. Pivot lets you compute pivot tables from data. Lets use `pivot` to calculate the average flight times between Atlanta `ATL` and other airports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Origin': 'ATL',\n",
       " 'ABE': 96.59897959183674,\n",
       " 'ABQ': 172.1290684624018,\n",
       " 'ABY': 29.252365930599368,\n",
       " 'ACY': 90.23557692307692,\n",
       " 'AEX': 78.87462235649546,\n",
       " 'AGS': 29.076743280093496,\n",
       " 'ALB': 108.65161923454367,\n",
       " 'ANC': 426.3529411764706,\n",
       " 'ASE': 198.09574468085106,\n",
       " 'ATW': 105.11235955056179,\n",
       " 'AUS': 117.13764946048411,\n",
       " 'AVL': 33.510327455919395,\n",
       " 'AVP': 93.94900849858357,\n",
       " 'AZO': 86.6842105263158,\n",
       " 'BDL': 109.44785031847134,\n",
       " 'BHM': 28.848588537211292,\n",
       " 'BMI': 78.14963119072708,\n",
       " 'BNA': 38.880419382261266,\n",
       " 'BOS': 120.84425582313462,\n",
       " 'BQK': 41.63483735571878,\n",
       " 'BTR': 70.18819188191883,\n",
       " 'BTV': 119.43465045592706,\n",
       " 'BUF': 93.706432748538,\n",
       " 'BWI': 81.02321908071394,\n",
       " 'BZN': 220.15625,\n",
       " 'CAE': 33.79744210162461,\n",
       " 'CAK': 72.89645776566758,\n",
       " 'CHA': 24.022727272727273,\n",
       " 'CHO': 66.9613478691774,\n",
       " 'CHS': 42.49556255367879,\n",
       " 'CID': 101.25268817204301,\n",
       " 'CLE': 77.47449470644851,\n",
       " 'CLT': 41.581799591002046,\n",
       " 'CMH': 66.05350605463249,\n",
       " 'COS': 161.14626865671642,\n",
       " 'CRW': 57.365990202939116,\n",
       " 'CSG': 20.638461538461538,\n",
       " 'CVG': 58.29219530949635,\n",
       " 'DAB': 54.343106674984405,\n",
       " 'DAL': 107.51469163706813,\n",
       " 'DAY': 63.95127118644068,\n",
       " 'DCA': 77.81844338944038,\n",
       " 'DEN': 168.22181282240237,\n",
       " 'DFW': 111.94913462808826,\n",
       " 'DHN': 33.231617647058826,\n",
       " 'DSM': 105.68818272095332,\n",
       " 'DTW': 87.08653664078773,\n",
       " 'ECP': 41.33196534427724,\n",
       " 'EGE': 188.8181818181818,\n",
       " 'ELM': 87.84615384615384,\n",
       " 'ELP': 171.9362244897959,\n",
       " 'EVV': 56.99230769230769,\n",
       " 'EWN': 61.98867313915858,\n",
       " 'EWR': 101.52093279471171,\n",
       " 'EYW': 87.044921875,\n",
       " 'FAR': 151.3846153846154,\n",
       " 'FAY': 51.07640067911715,\n",
       " 'FCA': 242.04,\n",
       " 'FLL': 84.61670182404765,\n",
       " 'FNT': 89.2876447876448,\n",
       " 'FSD': 132.47368421052633,\n",
       " 'FSM': 88.34068965517241,\n",
       " 'FWA': 75.94344473007712,\n",
       " 'GJT': None,\n",
       " 'GNV': 48.145907473309606,\n",
       " 'GPT': 56.39078947368421,\n",
       " 'GRB': 107.31177829099308,\n",
       " 'GRK': 114.30221518987342,\n",
       " 'GRR': 87.19142857142857,\n",
       " 'GSO': 49.15724137931034,\n",
       " 'GSP': 30.053024645257654,\n",
       " 'GTR': 44.07607607607608,\n",
       " 'HDN': 191.74736842105264,\n",
       " 'HNL': 546.5,\n",
       " 'HOU': 105.17070383912248,\n",
       " 'HPN': 109.62556165751373,\n",
       " 'HSV': 31.34819769602378,\n",
       " 'IAD': 76.36581920903954,\n",
       " 'IAH': 102.31285988483685,\n",
       " 'ICT': 111.22064393939394,\n",
       " 'ILM': 55.823125357756155,\n",
       " 'IND': 65.04984348663616,\n",
       " 'JAC': 212.4757281553398,\n",
       " 'JAN': 54.522334004024145,\n",
       " 'JAX': 43.31659192825112,\n",
       " 'JFK': 104.3519768563163,\n",
       " 'LAN': 90.23076923076923,\n",
       " 'LAS': 232.89032258064515,\n",
       " 'LAX': 256.5899419729207,\n",
       " 'LEX': 48.87252924566357,\n",
       " 'LFT': 78.40828856485035,\n",
       " 'LGA': 102.68517445230091,\n",
       " 'LIT': 69.1775396085741,\n",
       " 'LNK': 119.29885057471265,\n",
       " 'MBS': 98.26666666666667,\n",
       " 'MCI': 103.08773784355179,\n",
       " 'MCO': 60.877189043556356,\n",
       " 'MDT': 84.41851494696239,\n",
       " 'MDW': 88.65857142857143,\n",
       " 'MEM': 56.11916026020106,\n",
       " 'MGM': 31.892723492723494,\n",
       " 'MHT': 119.974930362117,\n",
       " 'MIA': 85.72141706924316,\n",
       " 'MKE': 94.42390670553935,\n",
       " 'MLB': 64.01593901593901,\n",
       " 'MLI': 90.73167848699764,\n",
       " 'MLU': 71.84965304548959,\n",
       " 'MOB': 52.496899545266636,\n",
       " 'MSN': 98.24233128834356,\n",
       " 'MSO': 240.26666666666668,\n",
       " 'MSP': 129.69842810013586,\n",
       " 'MSY': 67.13189171343677,\n",
       " 'MTJ': 195.62857142857143,\n",
       " 'MYR': 47.62226512226512,\n",
       " 'OAJ': 58.33685064935065,\n",
       " 'OAK': 295.140625,\n",
       " 'OKC': 108.98932219127205,\n",
       " 'OMA': 118.01823361823362,\n",
       " 'ONT': 252.33333333333334,\n",
       " 'ORD': 93.76928500916655,\n",
       " 'ORF': 68.94117647058823,\n",
       " 'PBI': 80.84571553802323,\n",
       " 'PDX': 283.55982905982904,\n",
       " 'PHF': 72.09481481481481,\n",
       " 'PHL': 93.60806835691274,\n",
       " 'PHX': 216.42130445731553,\n",
       " 'PIA': 85.57802874743327,\n",
       " 'PIT': 72.58607132827858,\n",
       " 'PNS': 47.029735234215885,\n",
       " 'PVD': 114.56133464180569,\n",
       " 'PWM': 126.81884057971014,\n",
       " 'RAP': 163.13333333333333,\n",
       " 'RDU': 54.620066109274745,\n",
       " 'RIC': 65.85651612903226,\n",
       " 'ROA': 57.06195244055069,\n",
       " 'ROC': 96.48102466793169,\n",
       " 'RST': 118.55202312138728,\n",
       " 'RSW': 74.23462897526501,\n",
       " 'SAN': 247.43282236248874,\n",
       " 'SAT': 124.2874558870709,\n",
       " 'SAV': 37.248983149331785,\n",
       " 'SBN': 85.35600335852224,\n",
       " 'SCE': 87.58974358974359,\n",
       " 'SDF': 53.38678295873868,\n",
       " 'SEA': 289.20415814587597,\n",
       " 'SFO': 279.49426976197475,\n",
       " 'SGF': 87.09448818897638,\n",
       " 'SHV': 84.34255898366607,\n",
       " 'SJC': 277.67333333333335,\n",
       " 'SJU': 184.041015625,\n",
       " 'SLC': 213.2850971922246,\n",
       " 'SMF': 272.2068965517241,\n",
       " 'SNA': 251.88867745004757,\n",
       " 'SRQ': 65.03754752851711,\n",
       " 'STL': 75.68986187125358,\n",
       " 'STT': 189.22590361445782,\n",
       " 'STX': 196.3783783783784,\n",
       " 'SYR': 101.62786259541984,\n",
       " 'TLH': 39.01092179991262,\n",
       " 'TPA': 60.845393024466425,\n",
       " 'TRI': 39.92660550458716,\n",
       " 'TTN': 95.16193181818181,\n",
       " 'TUL': 97.27774408732566,\n",
       " 'TUS': 204.35355029585799,\n",
       " 'TVC': 103.75757575757575,\n",
       " 'TYS': 30.09027777777778,\n",
       " 'VLD': 37.780971258671954,\n",
       " 'VPS': 43.24717691342534,\n",
       " 'XNA': 89.38354577056779}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "on_time_dataframe\\\n",
    "    .filter(\"Origin == 'ATL'\")\\\n",
    "    .groupBy(\"Origin\")\\\n",
    "    .pivot(\"Dest\")\\\n",
    "    .avg(\"AirTime\")\\\n",
    "    .rdd\\\n",
    "    .map(lambda x: x.asDict())\\\n",
    "    .collect()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting Scatterplots\n",
    "\n",
    "Another type of visualization that is of interest to data scientists is the scatterplot. A scatterplot enables us to compare the trend of one value plotted against the other. For example, we could calculate the relationship between `Origin` and `Dest` `Distance` and the `Mph` speed figure we calculated earlier. Are longer flights generally faster, or not?\n",
    "\n",
    "To prepare a scatterplot, we need to use `matplotlib` again, so we'll need to look at what its scatterplot API expects. The `matplotlib.pyplot.scatter` API takes two independant lists of values for the variables x and y, so we must compute them for `Distance` and `Mph`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+------------------+\n",
      "|Distance|               Mph|\n",
      "+--------+------------------+\n",
      "|   432.0| 439.3220338983051|\n",
      "|   432.0| 336.6233766233766|\n",
      "|   802.0| 373.0232558139535|\n",
      "|   731.0|471.61290322580646|\n",
      "|   769.0| 415.6756756756757|\n",
      "|   769.0|427.22222222222223|\n",
      "|  1047.0| 430.2739726027398|\n",
      "|   731.0| 359.5081967213115|\n",
      "|   731.0|466.59574468085106|\n",
      "|   731.0|  481.978021978022|\n",
      "+--------+------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mph = spark.sql(\"\"\"\n",
    "SELECT \n",
    "    Distance, \n",
    "    ( Distance / ( AirTime/60 ) ) AS Mph \n",
    "FROM on_time_performance \n",
    "WHERE AirTime IS NOT NULL\n",
    "\"\"\")\n",
    "mph.show(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Collecting Data\n",
    "\n",
    "Note that we will have to convert our data from existing within our Spark cluster's memory to within our local computer's memory where `matplotlib` runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[432.0, 432.0, 802.0, 731.0, 769.0, 769.0, 1047.0, 731.0, 731.0, 731.0]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distance = mph.select(\"Distance\").rdd.flatMap(lambda x: x)\n",
    "distance = distance.collect()\n",
    "distance[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[439.3220338983051,\n",
       " 336.6233766233766,\n",
       " 373.0232558139535,\n",
       " 471.61290322580646,\n",
       " 415.6756756756757,\n",
       " 427.22222222222223,\n",
       " 430.2739726027398,\n",
       " 359.5081967213115,\n",
       " 466.59574468085106,\n",
       " 481.978021978022]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speed = mph.select(\"Mph\").rdd.flatMap(lambda x: x)\n",
    "speed = speed.collect()\n",
    "speed[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling Data\n",
    "\n",
    "When I tried to plot this data, it took a very long time to draw. This is because... well, how many unique values are there for each variable? Lets see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total distances: 5,714,008\n",
      "Total speeds: 5,714,008\n"
     ]
    }
   ],
   "source": [
    "print(\"Total distances: {:,}\".format(len(distance)))\n",
    "print(\"Total speeds: {:,}\".format(len(speed)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is hard to plot 5.7 million dots on a scatterplot that will fit on a computer screen. So lets sample our data. We can use PySpark `DataFrame's` sample method. Lets take a 0.1% random sample without replacement, which will leave us with 5,687 or so data points - something we can more easily manage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5865"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = mph.sample(False, 0.001)\n",
    "sample.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we need to sample __once__ and then split the datasets out - otherwise the data for a single observation will be scrambled across variables. We don't want that! All our scatterplots would show no relationships at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5,865 x 5,865 records!\n"
     ]
    }
   ],
   "source": [
    "speed = sample.select(\"Mph\").rdd.flatMap(lambda x: x).collect()\n",
    "distance = sample.select(\"Distance\").rdd.flatMap(lambda x: x).collect()\n",
    "print(\"{:,} x {:,} records!\".format(\n",
    "    len(speed),\n",
    "    len(distance)\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fun with `matplotlib.pyplot.scatter`\n",
    "\n",
    "Now we feed the `scatter` API `distance` as `x` and `speed` as `y`, giving it a title and x and y axes. Note that we also specify a size in inches via the `figure.figsize` `rcParam`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MAgMRRERERLTpMBBBRAOj1x3258/O48kXzsNnaNjm9yCWcvDkC+cBYEPBiPnVFCZDZtljAVPD/GpqQ+3tts2YrUJERERE7cdilUQ0MOZXUwiY5fHTbnbYnzk9B5+hIeTVoSgKQl4dPkPDM6fnNrTdqWEv4pZb9ljccjE17N3QdrupkK0STTll2SqzkWivm0ZEREREfYaBCCIaGL3usC/ELAQMteyxgKFiIWZtaLsnjoYRTTmIphxkpSz+fOJoeEPb7abSbBVFiOLPJ2cWet00IiIiIuoznJpBRC3pRRr+iaNhPP3SZQC5TIi45SKacvCpB3d29H0LwkETsZSDkPd2DDduZxAOmnV+a32HJ0P47MN7y47npx7cOVDTGs5FooiuOYjbLoKmjgPbfBj1GQM3vYSIiIiIOo+BCCJqWq+KRva6w/7o8eliTYiAoSJuZ5C0XTz2of0b3vbhyVBxPwpBni99+52BqLUwG4ni6lIKEEDQ1GA5Gbx2ZRWHwn7sGff3unl9j7U1iIiIaKthIIKImtbLopGlHfZuKxSkLF0147EP7d/wqhml+mFlkGadnFnAwbAfF24mYLtZGJoC283i/EICn/vgxoM0m1np+dZV4Jvnb+Jv3pjH+w6M4eeP7+7bc05ERES0EQxEEFHTNssqD634xLun2hp4qNTrlUFaMb+awu5xH/ymhouLSSQsF0FTQ9Cr922b+0XhfDuZDN6Yi8LQFIx4Nbx5Pdb3ASgiIiKiVjEQQURNmxr2Ippyip1kYPBWeehXgxjkKVwPEwETE4Fc2yuvD6o+BaNwvl+5HIOhKTB1FVJKJOxMsdgnAxFERES02XDVDCJq2mZY5aFf9XplkFbwelhfreVNDVUgbrmIWQ4MLfcn2Xaz8Jta3wegiIiIiFrFjAgialqvi0b2UqcLC/Z6ZZBWrHc9sBhj7Sk3aTeDaMqBR1VgORkIIWC7WdyzI9j3ASgiIiKiVgkpZa/b0LIHHnhAvvrqq71uBhFtEaWFBUuDBO2ex7+ZOu7dOmb97onnzmAyZEIRovhYVkpEohY+8749ePb0FXzr4hJGhnQcngzA1LUteZyIiIho8xBCvCalfKDac8yIICJqULcKSfZyZZB2G8Tim51Qr67K4ckQfu9n3l0WgAp59S2TZURERERbDwMRREQNGsRCkr3GY5bTyJSbzRSAIiIiIqqHgQgiogZxtZDm8Zjl1KujsZmm4hDR5sXPKiJqJ66aQUTUIK4O0Twes9sOT4bw+CMH8cVP3ovHHzlYDEJUW01jNhLtdXOJiIr4WUVE7cZABBFRgwqj2iGvjkjUQsirs5jgOnjM6iutoaEIUfz55MxCr5tGRFTEzyoiajdOzSCiLWcj6aWcx988HrPaWEOjvzD1nKg6flYRUbsxEEFEW0rpcpKl6aWbaZS+m50pdtw2hjU0+sdW+GwgahU/q4io3Tg1g4i2lM2eXtrNebycM7xxrKHRP07OLCCbzWI2EsOLswuYjcSQzWY3zWcD0Ubws4qI2o0ZEUS0pWz29NLSQAuA4r8nZxbaPqq73nsxW2J99VbTGESDfM7PRaKYW1qDqasIGBosJ4O3bsSx5mR63TSinttsn1VE1HsMRBANiEH+gt9PNnt6aacCLdWuv3rvxTT3xm2WGhqDfs6jKRdCCJi6CgAwdRW2m0U05fa4ZUT9YbN8VhFRf2AggmgADPoX/H5y4mgYT790GUCu0xy3XERTDj714M4et6w9poa9uLyYwI24jYTlwm9q2B4wsHfC3/I2K6+/d24l8Ph/vQHbzSBo6jg6FcS4PxeQKAR1upmZwSBdf+jmOe+EoKkhtubAcjIwNAW2mwVk7nEiIiJqL9aIIBoAm72uQTdt9uUkD4Z9eOPqKmIpBz6PgljKwRtXV3Ew7Gt5m6XX33LSxvmFBABgSFeRsFycfnsZN+OpsjnD86spBCo6cJ2YAtNKnYrZSBRPnbqAJ547g6dOXehZTYt+aUe7dOucd8o9O0I4GPbD0FUk7AwMXcXBsB/37Ngcnw1ERET9hGF+ogGw2esadNtG00v7eQT+wkIS908P40bMRszKTUE5FPbjwkISn2hxm6XX38WbSRiaAkNTkLAzOLZvFG9ej+HM1RgeORIuzhnu1hSYZkfh+yW7qF/a0U6DPu0ply21hiOTwbJsKRbjIyIiaj9mRBANgKlhL+JW+TzlQfqCv5n0+0oR86spTI/58NC+MXz0yHY8tG8M02O+DQWtSq+/mOUU09b9poaJgImHD07gyI4gHn/kYLET3a0K682OwvdLdlG/tKOdBr2q/mbPliIiIuonzIggGgCbva5BM3qdjdDv8+A7MSpddv0ZGmL5oMQ9O4I1t9+tCuvN7m+/ZBf1SztaUese7FVV/XZ+JrAYHxERUXcwEEE0ALhsVk4/pLP3eweyNGhguy7OXY9jZc3B+w6MYTYSbek4lV5/oSEdsZSLg2E/xvxGcdS7WlCsG526ZoN0/TB9YDYSxdzyGr4/t4pRvwcHJnyYCJgDkeW03j3Y7Y58P3wmEBERUfMYiCAaEByp649shH7oyNZTCBo8e/oKvnNxGSNDOn54/yg8mrqhDlrp9Vc5At3roJhXV/Dy5SUICNy3K1R3H3udXVToOE8GDUTX0oilHLx+ZRUHw36oqtL3WU79cA/2c3uov/Q6g46IiGpjIIKIBkY/ZCOs15Ft9otvJ74oH54MYTxg4kN3bysLmADt6aD1S1CsdDT8I4fDxXNRT6+zi0o7zn5Tw8WbSdxK2LgRt/E7P3mkL45rPdXuQdt1cercck86e/3wmUD9idkyRET9jcUqiWhg9EPRznoF7ZotZNnJwpeDvpRiI1ot+Hh4MoTHHzmIL37y3rICm91Qel7G/SYe2jeGH3vXJKZHhwaic1R5D95KWHj50gp0VfSkeGs/fCZQf9qMBWGJiDYTZkQQ0cDodVp9Qa2MgGbTxDuZVt7vU0jaYRBHwwfhvNTL0qm8B2fmYxDIFS5VhICTyeDSYgK/+pUzuG/XMAQAOyM7linRL58J1H8G8fOh33GqCxG1EzMiiGhg9PvyetWyECzHxd+fu4EnnjuDp05dKBsp7mTWwqAvpdiIQRwN7/fzsl6WTuU9mM5k8eDeEUwETNxKWHjtyiqklEjZLl6+tIzTl5ahq+hYpkS/fyZQ7wzi50M/6/elq4lo8DAjgogGSr/UJ6imcrR7MW7he5dX4De1qnOUOzk63utaCN0wiKPh/X5eGsnSKb0Hnzp1oViX4+LNJAwtN76RzkpMDOW+YvzTtRiGDA3LiTR++6vn2l4Lo58/E6h3BvHzoZ+xMCwRtRszIoiI2qRytPvN6zFIAEenglXnKHdydLwTKbSzkSieOnWhanZHLwzaaHjh+H3p2+9gMW7BUAXmV1M4ObPQ82NZ0GyWTuk1HE2lASlhu1mYmgJDU+BmsphbWYPtZDAypGEpYXMUlbpi0D4f+t1WqDtERN3FjAgiojapHO12MhLH9o1g3J9LW794M4loKg1FKMXAwEZGx2sFGzpRLb4d2+zUCiGD0LEoPX6aArx8aRkSwLF9I31Vzb/ZLJ3Sa1gRCiAE7p8O4eJiEraTwa1EGl5NgamrsJwMxv1GMRjX632lzW9QPh8GQScz+Fh7gmhrElLKXrehZQ888IB89dVXe90MIuqBQfjiUkhbdzIZvHZlNZe2LiUgBPZN+NsWGChNOy50Ciu/MBb+//FHDm5oX1rdZr329tt564TS43f60hJsJwMAMHUVD+0b2/D5aZeNnKfS37UcF9+7vIKFuI3doyY0VYXtZvHe3cMY9RmIRC188ZP3dmmviGijZiNRfOHkeSwn00i7WXg0BaM+D379xKENfYZv9b8NRJudEOI1KeUD1Z7j1AwiGjiDUjSrkLY+Mx+DRxUAcisI3LMjeMcycs1Oe6i3NF0nUmg3us12LqX3/Nl5/Nyfn8YH/vAb+Lk/P43nz843vY1uKz1+CcuFkZ+6ELNy9RX6JcV5I+nspb/rZoFj+0axd2wIlith6ireu3sY436TBQOJBpQicn/HJGTZ/28El1kl2ro4NYOIBk6vi2Y1mo1R6Jj96lfOABIIeDXcsyOIiYCJrJTFjmcr0x7qLU3XiRTajW6zXUvpPX92Hk++cB4+Q8M2vwexlIMnXzgPANg34e/bLJnS4+c3tWJGRNDMHc9+6pxvJJ298ncrRzsL9ST6tWDgIGRaEfXCyZkF7BodwtGp2/dDNOVs+O8ul1kl2rqYEUFEA6eXRbOazcY4PBnCR49sx7F9Yzi+bwwTgdwXrtKOZysjQvWWputEEcyNbrNdS+k9c3oOPkPLHStFQcirw2do+LNvXuqLLJlamS2lx2/f+BASlou45WLfxFDfLeHZToNUMHBQMq2IeqFTf3e5zCrR1sVABBENnF5+cWklaLBeJ76VL3j1ttmJzt9Gt9mu4MhCzELAUMseCxgq5lZSPU/vrdeRPTwZwkcOT+BcJIbTl5ZhelTcvd0PJ4O+7py3w+HJEB5/5CC++Ml78fgjB6vu53pTk7oxHYcp4kS1dervbidXjyKi/sapGUQ0cHq5PnwraaTrrY7RyrSHetvsVHr5RlP2N7JCSEE4aCKWchDy3o6jx+0MdFW0PFrXruNVb8oQALw4u4gjk0HsnxjCuetxzFyP430HxjZV+n8rx3K9qUn1puN84t1TbWs7U8SJauvU3912/W0gosHDVTOIaCD1ai73RlePqKadVcM3ewXy0k5pwFARtzNI2i6O7ggiHPI2fV7aebyeeO4MJkNmWQG3rJSIRK1isKkTK6j0i1aP5Xr31M/9+el88Kn8+aBXx1d+6Xjb2t+Je5toM2ENFSJqVr1VM5gRQUQDqVfrw3diVKhyRMhQBby6gi99+52mv+z1upBnpxVGwJ85PYeFmIVw0MRjH9qPfRP+ls7LRo5X5ZdyjyoQt9yqmS2F0fZXLsdgaApMXYWUEgk7U0z/H/Tz0+qxXC8TYSFmYZvfU/68oWIhZrWz+T3NtCIaBL36u0tEmxMDEUS0ZbUyutOpNNLCF7zSUeUxv9bQChqlanXq3ryem4PfqZGs9Y7lRkbSqv1utZHwVs5Lq+n41aYTLMRsZKXE7jHfHR3ZkzMLiKYcxCwHASP3pzeacpCwXXz30i0oQhn40cVWj+V6U5NqTccJB807trURTBEnIiLqHgYiiKjvPX92vmwE/NHj03XnhjfS6W1lycyCTo4KbTSjoVqn7sqtJK6tpLBzZKjpfW3EesdyI8e6md9t5by0uixp1fM0OgTHzWU4VOvIPv3SZXhUBZaTge1mEYlaCAcNGKoCCNHWc9INlfeZUScjpJ56mQizkSjGh3TMzEcxpKvYFvQgKwWStovHPrS/7fvEEV8iIqLuYCCCqEs289zKTu5bs4XqGu249usUho0WzKvWqbuwkMCh7f6O7et6x3Ijx7rT52m9dPxa13at8xSJulXrCRRG2589fQXfurgE28kgHPBgyKPBdrO4fzoEj6b2/PprVLX77HrUytXHGB1qampDrUwEIBe82Rby4uG7xvDGXBRXllLYO+7Db378UFsLVRIREVF3MRBB1AUbGRHud53et2dOz8FnaCUdUaX4eLWOSKMd136tkN/qCH1BtU7drjEvhgwV3720hJjlIGjq2DcxhPlVd/0NVlHZOT8XieLu7cGy15Qey2aOdeW237wexeHJ2tveqPVWHylc27oKfPP8TfzNG/N434Gxlkb/D0+G8Hs/827MRqL41a+cASRg6Cru2RHERMBEVsq2X3+dChJWu892j/mQrpMRUk+1TISnTl0ovkfIG8Kh7aHivcEgBBER0WBjIIKoC/p19L0dOr1vzRaqa7TTu9EOf6e0OkJfqrJT91t/fRanLy0jYGoIGBosJ4OXL63g+L7RpttXLfB0dSmFIV3FnnF/8XWlx7LRY11t29dWctveO1F92/Xa2WgHvFY6fuHadjIZvDEXhaEpGPFqePN6DKM+T0uj/4X3++iR7R2//todJCw9pm9ej+LenaGy9tfLCGlFvwYLiYiIaOOU9V9CRBs1v5pCwCyP+22WL9Sd3rdw0ETczpQ9Vq9Q3dSwF3GrfKS/WgfvxNEwoikH0ZSDrJTFn9OtW8UAACAASURBVE8cDbel3a0qjNCHvDoiUQshr35HrYVoyinrWM5GonW3KQGIisdE/vFmlQaeFCEQ8uo4GPbj/EKi5rFs9FhX2/ahsB8X6my7mlaPU6XCtX3xZrK40oWhq3AyuYKU4aBR9Tw1ohvXX7XjWViho1mVx9SjKvje5RUsxm8HBNcLpMxGcgVTn3juDJ46dWHd89HovdzKtomIiKi3GIgg6oJmvlAPmmr7NreUxNzyWls6BY8en0bSzo02Z7NZRFMOkraLR49PV319ox28eh3+XijtSJ2cWcCJo2F88ZP34vFHDhbb1GzHsrDN77y9BFNXkMnmlos0dBUP7h1BOtN8KKJa4Gn3uA87R7w1j2Wjx7ratqfHfNg1Vnvb1bSrA164tmOWA0PL/bm03Sz8poaAqSGdkXj8kYN3nKdGdOP6a2eQsPKYHp0KQgJ483qsoUBKK8GhRu/ldgWeiIiIqHs4NYOoCzbz+vSV+za3lMTrc6u4b9dwW9LBC3PBS1fNeOxD+2sWqjw5s4C45WB+NYWQV8ORyVDNeer9UiG/0RT6ZmstFLYZDhiIWS4gJe6fHsZEwLxjWkCjqk2zmFtKIma5ZdMgANyxXOh6Kfu1pnAcmQw1le7frpT+wrVdWOlCCAHbzeKeHcG2BBI7ff21c/pR5TEd95s4tm8EZ67GEIla69aDaGUKV6PLaW7mqW9ERESbFQMRRF3QzPr0g7a6RuW+RWI27ts1XJzT345OwSfePbVucbrSjvfhyWAx2NPI8ev1MW+0IzU17MWZuWX8YDGJhO3Cb2i4a8KHe6fvrPVQus27wn68dmUVAHDxZgIeTW05ENZI4OkLJ89DEQK7RptbLrRdAbt2dcArV7oYGdLxnl2hDR2/bmpnALTaMTU0DY8cWT/ABOQCGZoCnIvEkLBc+E0N+8aHkLDrF0y9tJjA6UtLWIhZmAuaOBj2Vc2kYS0JIiKiwcJABFGX1Bv9LHSEz0WiuLqUwsGwH7vHfQOzukbpvj3x3JmOdwqqBQ5aHRXthxVNGu1IDXkEXrmyAo+qYkhXkLQzeOXKCo4fGCvbn5MzC/h/vz+PcMDAXWE/xv0m3rt7GD9YSGAhbuO4V294NYNKhydDuHu7D8+cnkM05UAAOLTNVxZ4Wk6mAQBHp0LFx4D1z0UzAbt66nXAmw06la50Ufi90AaOXze163gCGw9qeFSBly8tw29q8BsqbCeD711ewbE6BVMbXbq3XwvPEhERUW0MRBD1WGlHOLrmAAK4cDMBv6lhIpDrnA5SinGnOwW1Agdxy2l6mcfZSBS//dVzWErYGPcbOLDNh3F/48e8XZkUjR6zr791C+GACTuTRdrNYshQMaLq+Ppbt/BLH7ir6nSM166s4r27hzHuN6GrKo579Q2tavD82Xk8+92rCJo6pkImzi8k8IOba9gWjOFgfgnPtJuFrCiF2Wgwqh3TFWp1wAG0HHRqR7t6kXnTrukfGw1qVCuOWq2IKnD7OP3ly1cAmQtkKYpSXLr3T795CRcWksV2HAz78OLsIoDNN/WNiIhos2IgggZSr1Pp26l0JD9uuwiaGmw3i4uLSUwEzIFLMe50PYxamQ/zqynELbehAMhsJIpnT1/BP5xfxEoyDY8qkLBcXF1ew6jPAzebhSKUutdVOzMpGj1mhaVMFeV2neFsNltcyrR0ucm1dAbXoyl4FAVn5laxf1tuZYudI148depCy/fMM6fn4DO04nEOmBqSdgZn5qPFQIRHu7MOcrdHqKt1wJ86daHrtQQKn1VvXo/i2koKh8J+TI91L9upnZ+VGwlq2BmJY/tGcGlxDTHLQdDUcWRHAHZFwdTS+8rJZGGoApGohcmQCZ+hQRESl2+tlRWmfHF2ER85PFEWnBiEjBUiIqKtjIEIGjjNdgD7PWhRmpYfNHVYTgaGpiCRX4li0FKM25kOXk2taQwhr4Zoyin+f63OfOH6OTcfxZrtQgjAzmQBJ4NUOgPbzWDnsBcQou511c4CeY0es3DQRCzlFEeG19IuIqsWIHKd7HORKMb9HrwxF4WhKdg17MViwsbFW0kk0xm8ayq04Sk/hWBIwajPg1R6DfGUi6yUiFsuRn0eKEIgmnKaDkZV3q8Hw76yDuZG7t9u1xIo/ayK5a/N8wu5bKfSzJvCv+3+jOqHaUcFhayfh/bdnkYUTTnYFigvmFp6X/kNDbaThaYILK+l4TM03Iyl4Tc0pN0MXr6cqzehqwLf0RX8/s+8u6v7RERERK1jIIIGTjMdwH76Il5LaVr+gW0+vHZlFbabRdDUisvVdTrFuN3Bmnalg1drV72VFQq1IhqpsL+0loahK/BoAjHLxVrahSoEErYLOyNx/3SuKGGtwEK7O7WNHLNHj08X58irQmJu2YKExI/sH0M05eDqUgpzt9ZgelSYugroKjRVgYCFoFdvSwHRcNDEYswqTg/xaApMXYXpEcXVE379xKHi9psJRlXer+/cSuCvX7+G+3YNt6VmSrdrCZR+ViXszO1sp5tJjPtz2U7nIlHMLa915DOqn1aTaDTrp/S+undnCN++uAQdAm4mF7hYczJ4z84gXp9bhaEpxXoT3764hNlItG8+14mIiKg+BiJo4DTTAeynL+K1lH5BH/UZOBTOpdAH8+3udIpxvwZrarXrI4cnas4Hb6QzX3r9CAC6piJgSCyvudAUASFQXOIyK2XNwEI7O7WNBoJKlzI9fyMOXRUYHvLg6oqFlZSLcNCD71+LYc+oF1JK2G4Wdr6WhO1myrbVatDkw3eP44++fhEeVYVXF1izM0hnMvgXHz6AX/rAXWWvXa9I6HoFR2/EbPgMDTfiNvZO+Dd8/3Z7Gd3Sa81varDz2U4xK5cdkXt/F1PDQx35jOqn1SQazfopva/uCuem+rx2ZRWWm0HQq+ODd41jPmrByAfAAABCYGRI76vPdSIiIqqPgQgaOM10APvpi3gtlV/Q94z78bkP7u/aF+p+DdbUateFheSGpn4Urp/tQRPzKykAEoqiwNQVaIqC6dGhYpHQejUmFuMWvp1f0vHIjgAMTWupU9tsIKiwlOkvPvM9XFtOwdAVGJoC28kgtpbFkC4AIZCwM/CbGu7ZEcSb12N3bKfVoMlaWuKHdo+ULSH6nokg1tKVpQib3+eE7eDu7bcLjsYsBwFDLU5TAjaedVJYjvPrs8uQkLhv1zAuLSY6MjWiLNtpwofX5+7MdgqaGgJm+Z/idnxGzeYzLb4/t4pRvwdjQzqW1hwsJ9IY9Xu6kj1QLdi0XqHUymDRtqAXP3xAK94Ps5EofunLr2PYq5UF2+6bDvXV5zoRERHVx0AEDZxmRjUHZVm3dk1laEW/BmvqtauZ41Wt5sCLs4vYP+FD3HKQtDNIZ7IY9xmw3Qz2T/iKtQ7q1ZgIeXX88P5RzEbi+M7FZbz/wFhLWSSNBoIq9yMSzdWGKIwKm7oK281izG9iXz57oHB/bKRmQ6X51RTetWsE907fXnaxVuZIrUyPRguOBk0911lv8/275mTxQ3tHETA1XLmVxJMvnMf908NtKyJZazneg9v8uLCQQGjodrbTyZkFXF5M4EbcRsJy4Tc1bA8YxWk0rb7/0y9dxmTQQHQtjcWYhbeuxzDq02HoKiaDRkv72Gj9jkIx2G/lA3WHJwMNH9f1MicOT4bw/gNjmLkeyxf31XF0KghdVe+oN0FbS7/XgyIionIMRNDAaaYYYrdTsQdRvwZrarXLUAWeOnWhoS+b1UbeSyvsrzkZRFO5lUru2RG6o2NVr8ZE4b9w8HY7W/nS20ggqNp+LCVtGGouG8LQFNhuFlJKTA57i/fHm9ejiFkuQl4NE34DjptBJOq2XEC0MMr+xtxK2XKn1a6Xepketfa5kClQ+P/tQQPXV1M4uM1fNzjUjDumf8Tz0z9iNvaMb3z6R+l+3709CMfJ4DtvL+F7V1awa3QIj314f3GKDQBcWszVwfAZGgKGiljKQWQ1hY8dDbdlH/2mhm+cXwQE4GQlfmTPCMb9ufPx5dNXMBEwW7qXatXvKEydurSYwLA39xXj+1ejuH96GCFvY9Mn1gs0fvr47mJb+LlOQP9OMSQiotoYiKCB1OiIeKdXcNgM+jVYU61dV5fXkJUSuqY29GWz3vSOxx85WDaCBgD7JvxlncRqzkWiiK45xdHYA9t8GPUZd2QEVBudK7SpkeKbpR37avsRDpiwnAxMXS0uh7hnbAh7xv3FYzG3vIadI0Nl57XVL+aFL/rbAwZia7lpBa++s4K7twegKMod10u9TI9a+3zPjvKCo3vG/fjoPeG2LstYGQRJWG4uAJCv2wBsLCOodL8X4xZuxNOYCBgImhqOTAbx4uwi9k3cPkcXFpK4f3oYN2I2YlbumBwK+3FhIYlPNPB+1a6z0n0c95sIeXVMhUwk09niah2W4+If317Gh+7e1tK9VKt+xzOn53BkMoh0JouAoUEIAQC4uJjEsb2jbcm0OjwZwkcOT+CZ03NYiFkIB008enyan+tbWL9OMSQiotoYiKBNr5fTHgZBvwZrqrUrHTTg0dSGv2zWyzZoZQRtNp9qDwEETQ2Wk8FrV1ZxKOzHnnF/2etKt315MYFffvYabFdiW8AoS1WvV3yz3n4c2RHAdy4u4/hksOz3SgMe7fxiXro9J5PFmfko4ikXcdvFb/3Y3Xdss96x/8z79tQMflW7XxvpkDeqMgjiN7X8kqjtyQgq3e+Li0lkslkk7Qwi0RQgBLYHjLJzML+awvSYr+z6qVcktVSta3hIV9ad4jIbiWNkqPHro/J81qrfsRCzcGzvaHEpYlNXi8sRtyvTajYSxYuzizgyGcSxvaOIW+4dAR7aWvp1iiEREdXGQATRFvf82fmykcUfPTTeN1/mKzulTzx3BmP+xgv71cs2aKWjfnJmAQfDfly4mYDtZotTIs4vJPC5D+4ve13pqPiFmwkkrAyU3OBwWap6I8U3q+2HoWl4/4ExhLx61d+bX01BU4BzkVix9sC+8SEk7Nsdx2YUtvfqO8u4urIGU1Oxa8SEnZHFTmBh3+dXU5hbXkPayZTVOigc+14GvyozbbYHDERWUzgUbn76x3rLyy7GLMQsFwKAz6MiupbG2zcTeOWd5WJbmpkaVfl+t+IWVhIWXrm8VCwceteED96RoXWnuKysOfiRA6Nl22/mXqpVvyMczE3VKSxFDACQEroq2pZpxdFvqtSvUwyJiKg2BiKItrDnz87jyRfOw2do2Ob3IJZy8OQL5wFg3SkKjWi1eFit35sa9jZV2K/etJMvffudpkfQ5ldT2D3ug9/UcHExiYSVqy8RrKgPUTkqbmgKACAjZbG4ZGmq+npZO7X2o172hkcVePnSMvymBr+hwnYy+N7lFRzbN1r19fXMRqKYjcQwt5RERkoYqgJA4nrMxs5hL0JeHX/89R/g0q01OJksxnweDHlUvHE11xHdPe67o4Pfq0ylamn9n35oF9bSsqmgSCPLy9puFpmshKoIDHk03EqkkclKGJrSVEZMrfd7YSYC283Cq2sY0lXYThavz63CcrP47Z+8p+4Ul/cfGIOuqWXvUa/jdkcAp0b9jkePT+PF2UWEvDrumw7h3PU4VlIu3ndgDD9/fPeGC4DOr6Zw7noM9+4KArjd6bQcFy9fXmKhwi2qX6cYEhFRbQxEEG1hz5yeg8/QSkYWleLjGw1E1Jv6ANxZK6HQaaj3ewfDvqYK+5WOvJcWbjw5swBDFWXp68D6I2iNBkJKR+cSlgu/oUJVBIBcSkSzqeqtZBAIAJULaspiCxpXOB9uJgtVEUg7WaSy2dzcfwkIAdiui+9cXMLksBdjPg9sN4uY5WL/hA834jY8utpwB78yQ+fR49NtCYqV7k9lWv9bN5JN185oZHlZCUARwLjfg3h+CoMiAFNXml6Ottr72W4WblbC0HP3raEIuFmJuZXqwa3SKS6F87qcsHEjZmE56UBTBB778H5UU22Z4Vr1O/ZN+POvc/HBQ9s2HBSo/Ez4wUIcL19awfH9AuN+E4txC9+7vAK/qbFQ4RbVr1MMiYioNgYiiLawhZiFbX5P2WMBQ8VCzNrwtmt11L58+gpSTrZmbYZ6adcAygr7aYrAkEfFn3zjEi4sJKt2eKoVboymHFyPWkjaLmw3C9vNwNBUjPsNfOpjB2vuU6OBkNLROb+hIma58HpUCACWkymmql9dXkM6aOCJ587UHMWtzA75zPv2NPTl2s5IHNs3gkuLa8Vilkd2BGBnKsMT9RXOh6oo2DnixZWlNdhuFulMFnvGhpDJAueux6EIgaCZK05YyPpYS2cwPebDFz95b0PvtZEMnUazb9qV1t/o8rLv3ErgRizX2fd5NPgNDcM+T83XN/N+AgLZbBZuJpd1kclKKAKQsrHjYaddfOfSMhQB7AiZOLDNX7fWQqP1O9qd7VJ5zu7ZEcTLl5YxMx/DwwcNvHk9Bgng6FQQihAbmqrBJSAHF+tBERENFqXXDSCinNlIFE+duoAnnjuDp05dwGwk2vH3DAdNxO1M2WNxO4Nw0KzxG42bX00hYN5Zz+GNq6vFTkWh01BY1q/e782vpoqF/R7aN4b7dg3DzUqYmoKszBYDGtWOW2lHpvCeQVPDzXzAReTzBLKyfie9sMJByKsjkc4g5NVx//QwLiwky15XGJ0LefXiHPp7d4bw0P7ctIiVlIupkImslPBUrABS2v7CSHA05dR8TS1Tw14YmoaH9o3ho0e246F9YzA0rek504Xz4Tc1qIqC3WM+BEwduqpAFQK6KrCy5mDHsAnbzRZ/z9AULCXTTb1faYaOouSyBnyGhmdOz9X9vWaOU73rqxlTw95ilkNBZYbLiaNhKIqCw5NBHAoHEPTqUBSBAxO+qq9v9v18hoYhjwZVFUhnslDV3LU97vPUPR6F4zUftbBn1Ivp0SFkZK5wZ+m92C8qz9lEwMSDe0eQzmQRiVpw8kG3woogQGvndCP3GxERETWHGRFEfaBXa6A/eny6OOIcMFTE7QyStovHPlQ9Pbtauwujhx411523MxJTw154akx9EBAImBou3IjhzHwUSTsDj5rr0BaKHDpupmwVgdIOW2HKQ2nthZDXg5BXx0rSxm9/9RymR4fKlsw8dW4BWZlFyOvBgW0+jPtNRKIWNFXFBw9tK75PNOXUHUVtZoWD0tG5wnF683oUhq7i0HYDt9YcbA8YdUfmNzJ6X8jKWEnaiEQtLCXT0FWl7rmdjUTx5dNX8MbVVQgI3LcrVDyPByZ8eH1uFYamYHRIR9RyinP/gdwymBduJgDkghAxy4WuKsVz0IhWM3SaOU6tFrWrHCk/GPatW9uhNF08NKQjlnJxMOzHmN9ANOU0NYe92hz4XaNeXFtJIWjqZffvzhFv3eNROF5ORkIREivJNNbSGXzj/CI+cHAc86utFTTtlGrnbM3OIGDeXvkkaWWAwO3faaVQIYtgEhERdQ8DEURN6FTabq++ABfS3Uvn5D/2of0NzckvDZ5oCvDypWVIAMf2jSCacrAQs5GVMj+Cfrujdt+uEM5eXcEbV6PwqAp0BVhdSwMQSFhpbA8YeH0uV+Rweux2kcMH9wzjOxeX8O2LSxgZ0nNTMwSwvJar+/APby0glnKgqyqO7R1FNOXgCyfPQ8mP2kMqxeU237t7GEvJNMZ8FZ3edUZRW+3EVpse8nf/FEFszYHf1DARMKu+f6tL0hWu0+sra7i4mMSQJ5d1sT1o1ky9n41E8YWT5zG3tAa/oUICOH1pGeN+D3yGht1jPrxnVwizkTjsjMQjh8P49PHdAHLTbb5/LQpDE8hkgeVkBpoi8D/cP4mTMwv40rffaeh+CQfN/FKat5P1GsnQaeY41Spq9+CeYTx16kLDdUtenF3ERw5PVK2RUKpaQOpcJIpoKlfotJB9sN59Xm0O/L/96aO4tJi44/79xvlbdbM+CsdLVYCryxYMTYFXV5BKZ3K1F1ooaNpJlefsyq0k3ri6ivunhzEZMuG4meJnRrXCqI3iEpBERETdw0AEUYM6mbXQyBfgTgVBPvHuqWLgofAe36hTs6CgNHhyLhKDP9/xubS4hof2jQGjQ3DczB3LSwLAp//iFShCwJNfPUBRch2hf5qP4WffuwsAEInZ0LVckcMH9wzjr16bx3IyDVUBbkQtRFM2PKqKyfyqDVeWkkg5WewavT394vpqCitraQx5VCwnHeiqQFYCL567Ca+uYHtFB3e9oMJGKrNXBpvG86PiFxeTxUBE5fuXLQUZt3BxMYnlRBqjfg9mI9Gq56b0OpUAJgIGAOCusB/jfrNm1sfJmQUsJ9Pwm1qxxoMQArabxV3bzNx0FLu8+GDpe/3w/lHMRuJYWXPw/gNj+OEDY8XVExq9XxrN0Km8F2pl31Q7l9U69A/uGa7b1nqFKR9/pHZNkWrvDeQCUlPDt+uVNPo5Um0O/OHJ0B2BwwsLyboBs8J1JYvVSyXcDKDns5qaqyLSeZXn7Ebcxv3Tw8XMpMK/kVhzhVErcQlIIiKi7mEggqhBncxaWO8LcCeDIKXTBq6tpHAo7Mf0mG/d9ygNnhRWhgCAmOUAyHXUI1G3akfN0BTYjouVtTTSbhYeTYEqgISdSwnfPe6DR1eLRQ4//9dncyP1poYJvwHbzGIt7UICxU5zOiOhCFFcFeJWwkIkakERwITfwFIijXjKBQSQBKD5DVyPpjDqNxoOKmykMntlsOnANh9efWcFy4l02fKHpe9fCHwsJ2xcWEgAAlAVYDJo1Dw3pddpws4gaGqw3Swu3kxi3G/WHOGdX00h7WbLRtJzUywc2BlZ9Tx++fQVXFpMwMlI+E0N9+wIwqOpCHl1XFhINn2/NJKhU+1eqJV9U+tcVnbonzp1oW5b2zlS3o3sp/UCZoXnU04GO4IGlpIO7EwGu0aGcHQqiHSNgqbtXo63VQnLxdDEUNlj02M+6JracGHUargEJBERUfcwEEHUoE6m7a73BbhTnZfSTl0slQsgnF9IwG9qxcJvtd6jNHjiNzXYTq7oZTA/b7veSOKIz4Ory7nU9ITtIiuR6zR7taq/+8bVVfgNtRh0MHUVmqogk83C1FXELAdejwqfriKb70NdvJnMFVRUgNWUA11V8sEKYGTIg4BXx1IijbSbQSTqNhxUaLUye2Wwadxv4u7tAURiuRoO1d7/8GQId2/34Y//4W1YThamruA9O0PYM+6vmdlQep0WzkshoFDt2Ja27wcLcdhutnicbTcLI5+VUmk2Es1Nk/Fq8BsqbCeXHv+eXaFiQGkyZBYzOW7FLVhOtlgQtFZntDRDp5qq90KN7JtGz9N693atQKFHFcXpHIYqIJELiNXrbHcj/X+9gFnh+d/+6jksJ9LYPe7DgQkfJgLmHftZ0GowtB1B1PWW7wTak7nAJSCJiIi6h4EIogZ1Mm13vS/Aneq8tDp6DpQHT/aND+F7l1cgARzZEVi3EN/uUS+u3ErCzQJej4pYykVWSgS9etXfFRB3pIsL5DrKMcuBKgSGdBWRqAWfoWEhlsKthI2AoQJCIJpykHYzUASQyUqM+T3w6iqWMxITAbOp9PpWVQs2KYqC3/nJIzU7Os+fncez370KRQhM+HU4GeDsfAxBr4792wJVz03pdVooMGm7WQTz0wBqnZcTR8M4e20Vc0trgJSQyAWH9o77qhacPDmzgJGh3L2wls5gZS1f8PCtND58eBsmAiYuLyZw4WYC2azE6pqDrARUReDyYgJPv7RWtTP6/Nn5soyIR49PlwUmat0LtbJvGrHevV3t3F1ZSuanF6nQ1Vw9DQHgwb0jdTvb3Ur/Xy9gdngyhN/5ySPFDn5gneuj1WBoO4Ko6y3f2c7MBS4BSURE1B0MRBA1qNNpu/W+AHeq89Lq6HmhvYXgScJ2cWzfKASAhbiNmLWGkLd2IT4nKxEOGrgetZCVuWUIR4d0uNlcR6VyFPK+XaFcR08IGPm6Eqm0C4+qwM1kEYnZyEgg6NXgMzR88/wi3GwWXl1DyNSQtF0kMxKakqsdMeTRYDkZjPr0rhSiK6Smxy0H86sphLwajkyG1h1tLSxnmZESmYyEoecmnpy5FsW2oLfquSm9Tsf8Bg5u8+PCQgKhIb3qsS04PBnCr584VLZqxvF9o/j08d01R/aP7Ajg9MUlrFouFABW2kU8I3H67SX8sx/aWZxOkrBdCCGgCmDc78GNuI0jk8E7OqPPn53Hky+ch8/QsM3vQSzlFGtGFIIRzdwL1YpD3rMjdEe2wnr3drVA4Y6QCT0/DeW7l2LFKS2Xbq3h+L7cSiLVOtvt/BzZ6JSHZjIAWg2Gzq+moCnAuUgsN4XL1LBvfKiYNdOIyvcuLN955lq0ZjYRERER9TcGIoga1Mu03VY7L+t1VFodPS8oXWaysISnlMCRyWDNQnyzkSiuLqWgayreNRWC7WZhu1kcCvuxZ9xfdVT708d340bMxq2EjZjlYGUtjTG/iXdNBXFmPgqhCAypCkZ9Bu7dFcLpt5dzK2UAcLMSPo8Ky8kAEAgHDVhOBrabxe78Mp+d9PzZefzxP7wNJ5PFmM+DyZAJRVEa6jQWlrPUFIFINLeEpa4CsVTt8195ne6d8OOXf3R/Q9fp4ckQfv9n3t3QfhWunYBXR8xykbBdqIrAiE+Hoav4m9cjGPbpyGYlItEUfB6tmImSsNyqndhC4OX26LlSfLwQiDhxNIwvfu0C3kjYsN0MslnAyWSxf5sfT526cEchzWw2i7mlNQghEFtzMKSr+OLXoggHjbJpFOvd25WBwieeO4NRf+5PaMxyEDByPyesXAe7Vie9XZ8j7aob02gGQKvBUI8q8PKlZfjN21N4vnd5BccqVuao91lV7b1NXcNHj2zfUDZTpwoAExER0foYiCBqQq/SdlvpvDTSUWl19LzWe7x0YRFxy8X2kAFF6FXTsE/OLOBg2I8LNxP5GgQKbDeL8wsJVlIxwwAAIABJREFUfO6D+6u+z+HJEJ742MHi/r95PYp7d4YQDnpxfiEOKSUcV+JGzIK4CvgNFW4WuG86hIs3k3AyWWwLGMhKwHIkRn0Kdo8OQVWVqtMO2mU2EsUff/1tQABjPk9xPw+F/Q2lpoeDJq4tJxGzXNhOFhCApgiM+DzrdjgX4xbevB7Fuesx3IpbNTMbWjXkEXj2u9cRTTkAJExNhaGrCAfN4iolWQkc2RGCEAKWk4Gp5wJC/nwwrbITWwi8lAoYKq4tr5XVYoim0gByU3NWkg58HhUTAU/ZNV5I55+NxGDqavG9Ly8lkbQzuJWw8fDBibLfaaZTW9o5Dpp6PtCF4uox62UTFYIlzSxvWqobRS9LO+qGKnA9ajVcELSg2iocxcU6St6n3mdVJ7LROlkAmIiIiNbHQATRgGg2CFLoqDiZDF65HEPMcuBRFTx7+gp+Lz/q3ezoeeUI4mLcKusMORkJv6EWa0wAgO26OHVuufg75yJR3L09CL+p4eJiEgkrlzIf9OrrzmkvPP/UqQuIphwsxi1EUy4EAEXJ1ZK4sryG7UEDwz4D434T434TWSkRiVr4zPv2tDwCWm30tHCcaz02t7yGZNpFyJsbHbfdLFQh8PbNBHRNXfd90q6LSNSGrgp4VMDJAmk3ixP3bKt7jr5w8nxulRFDhUSufsGNmI0nPnawLZ2sQu2KoKnBdlwk0xJrTgYjQzp8xu1pL4XMmu1BA2/dyBXChAR2jw5V7UiGgyZiKaeYCQEAt5Jp2G4W0ZRTDHYlrNxUoIuLyWJx1OKysSXHfzJklmUrGJqCS7eS2BEy4eRXWWm1A19WI2ViCC9fWoEAcHhy/RopwMY7wp0uelnZvrjlQhGirLjrg3uG1w2k2BmJY/tGcGlxDTHLQdDUcWRHAHbJyhwnZxaQzWYxG4kVX7M9aBTPSSey0boRyCEiIqLaGIgg2gSqdZLnV1PQVeCNuSgMTUEg30H81sUlzEaiZRX0G12Cr7LjdPLNCFShIJ3Jwm9oUADYmdxUCwAY8+l460YCAVMr/s7VpRSGdBV7xv2YCOQ6UpcXE7gRt/HEc2caChAUOoGXFhMYHdKwmHQAF9g5YuBG1MbNRBoP7BnFrYSFizeTuJWwMeY3AKClVO5q+/6Fk+ehCIFdo0PFx774tQvFZSQnQybemFtBzHKwmkxDVwXSWQk3I7GcTGN6dGjd93npwk2YugJICVcKGJpAwNQwcz1Rs60nZxawnEzDb2rF1S+EELiVsNvWyfqzb15CKp0BRK7YqJPJFbdcXktj1G8Up73snfDjxNEwTs4sYM3JFOs0FB6vbMujx6fx5AvnYbsZWOkMkukMLCeDd+0IFDuKuWtNLQaxSpeNvZWw8IOFBBbiNsJBE46bKWYrmLpavC4FbmcuAK114Ms7xy6O7xuFBLCYsBFNrSFo3q6RAtwZsNpoR7jTRS9rrU4S8up4/JGDxWs1k8niRszC9+dW8bWZG3jsw+XLrRbaWQgSAUA05WBb4Ha7z0WimFtag6mrxc+pt27EsZbPMgHW/5xqdppFN1YvISIiotoYiCAacLVGVod0BTPXYzA0paxDOjKkt9QhreyY3IylEE9loCgZjA55kLBcJCwXHl1BwNSQSrv4xxtx+A0N9+wIFkefD4b9OHstineW1mC7GWSyEnHLxbG9ow2PDBc6gb/2lbNQVQU7h70QAshkgXDIwK1EGknbxVs34hBCQFcUbA8Yxe0W9qfRTku1TtlyMjc94OhUqPjYGwkbAPDuncMAgHG/gVtxGyk3g3RWQFVyiepCAOduxMsCQtXex3azCJgadFXBzpFc4CKbzWIhZtVs6/xqCjHLge1kkM5IGJqC/5+9Nw2S5LzP/H5v3nVXH9Xn9JyNGcxBgABBggOCIEXxwIorKSybktchmqFQLOyItT5wlyErdh0W6Y1dK7RyMMJUhL0M0w4uYYdEWeSKK0ogAYoyBXIAkhhghjPTnMacPX1Vn3VX3ukPWVVTVV3Vxxy4mE8EomcalVlvZr6Zk8/z/v/PMxBXcf3gnpCsmaUi19aqxDUJVZbx/ABFFjhugOWG39fe9tJ+fDud708+NMn8Zo0v/+AGduPYDUWiYvusVUyGkwZpQ6Vuuy3jQ8sJxYqNqs1/OreEJkuMZXTGUjpn5wocySUo1OxWNcZAPEyIOTl5+/vvlMB3k+PmvTiZjbc8UrrFqeb8rlgOD46lAVoRp+W6A6J/tGk77rd57k5E/bkLeTzPZ3algq5IDCZCr5Av/d1VDueSPdu/+o2zWA/NTNvjecMqmN0ZWt5JdckblV4SIUKECBEiROiNSIiIEOFtiPbVv7mNGmMpfcvKqu16bNYcBmIKQRBQrDusVWxSuszzl/J7NmbrJibn5ovENRnT9fAD8IIAWRZ4vs9IyqCx+EwmprQqHwAShkzN8UjHVASCUt1BkQVJQ9lTqfzx8QwfOzG6hUw0ozqvrFbxfBhMhkacuVRIUJ49c5Oa4++JtPQiZbbrE3R1v1uuh2h0v6+WTWq2S9UODRVlKcD3BUIIprIGCLHlGLu/J6ErmLaHH/it35Utj9F051jaocuCzaqD0hA9CjWb1bJFSlfQ9ou+2+0Wz13Ik9QVPD/A9X3qtofnQ0CAJkvkyxYIwWdO7+8wjdzt+a7ZAb/68ETrmp65tk6p7rTafaZHEpy5ukGqkb7w4uvrFGp2o31F4AZBaIYZU3hkKsty2WL/ULxVjTGaCtNaNEXGD4J7SuB7CVbd4lTz/y0U6pRNF9v1ODtXQFckVDmcH7tp0bjf5rm9iPrcepWlUli5dHGxiCToEDrThsJ61e6Y17sZZ9pQKNUczEZqT1M0Shv9X1HaY14dz+doLtGqMtrNM+R+CzkRIkSIECFChO0RCRERIrzN0E3sXp3bpFRzSBq3CX/KUFgqupwaT/HKXIGq7eL5AblkQ7DYJdlpRzcxqVguuiLQFBVZFjhe0CBSEr98PPRKOHN1jY2q07GfS4tlxtIGHz42AsDzl/IoEh2+Erstke5HJp556hBfefEGjx8aZL1icWW1ymu3iiR1mdWyxYeOjeypJL4XKdMUacvn9Ibvw2rZbJHLlC5TrLsEgSAVkxlLx5ClMIa0+xi7v+fd+zL8YHYNXZXxfZ+y5VG1XH7vI71NPSE0AkxqMgXTwfH80CwwCLA9n3zJ2lKFsVcsFOq8eyrDi1fWcTwfWYAfBLg+ZOMyTxwZRFcUXphZ5XAuuecWhG4xZjqX4EdX18mXTIp1G12RGU5qTI8ksb2ATFwlrsnkyxYxRWYsoSFLgisrVd53aBBNlfmTTz3c8R3dZfz3isD3Eqy6xalm5YPpetzUZDaqNnoj4cX2At5zIIMqy7uqWrqf5rnd99bcepWzcwUemcoynjF4PV/m2mqV/YOxMMqFsIJnKKFtmdc7jfPkRAbH9Xh9NTQSTegyD+QSnJzovU13zOvlfIWzcwXiusIDo+nWmLd7hryZKUgRIkSIECFChEiIiPAWwf2IUbuf0WxvZuxbN7EbTuoU6w5XVqstIaJsumiyYN0NvRsCQm+CkukiSYInjgyhKbsjO010ExNDkanbHrmUiun6SJLA9QPSxm2CPp4xWlGTTaFgs+bwxJHb0X1JQ8GyXUrmbcFityXS25GJyWyM66uVVul4UpcpmS7rNRvTcTtEhZ1ISy/BYzChIQnRcWzDSR0/CLi4WGqRy2xcwyc00tRkGVkSFGs2uipzabHUETl5dDSxJerzxESaqu2xUrEZTRv83kc6e/C7YXsBTx4d5nszK6EQIQTZuErSUJgajN+1T8RkNkZRVxhLV1gumbh+KHSkdJmJbLyvaWQ7tjvfVdPh67OrmK5PQpc5NBjHdX00WWoR+oSu8OlGCsjn/uIc4xmDl69vYDW8IIIgoGQ6fefR/SLwvQSrXuKUKocCniTCiiBdlcjENE5NplvmqnfTRnMvnk/d99ZSyeKRqSyHckkATk2mubleI1+ySOhKK4b34NDWSNz26oXRtMFnTu/vmMNHRxN84+w8aUNlMmNQtjyurdX4zfdO9Rxbd8xr0lCoWR7n5ostIWI3z5A3KwUpQoQIESJEiBAJERHeArgfMWr3M5qtfd+qDH9/eYVvvrrAk9NDLXJ0J0Rgt9tsWTEeSfDTG5tsVOyOUvO4KrX60r97KY8kCVQhSOhh5UQ/stNvHN3E5D37s7x4ZZVbm3WECMmo64EkFPKlOoaqIEkSv/fLR5jNV1v7e3J6iIrpcubaOhXTRQhYK5sEwHcuLhH4oeHl9Eiyg6RvN65e5+npU6N89s+WoVE+3jQqHEsZzCyVGU3fJik7kZZegsfvP30M6PSa+K1PhEaY/+Lr5/EDv0UuN6s2L13boGy6Db8GkLyARw+kW3Pzo8dzvDCzytGRJMslk42qQ6nubjH/2wlNMpxLGRwaTnREZ94LM76WKCMEJ8bT2F7AjfUq+7IxdEVqCUrN79pLL/63zy/w6q0CNdvDUAW24/Hjm5tkDYWPnxprVcwU605LUGnufzqX4OxcAQjnoiZLb3ipfS/Bqpc41V75ULZcxlI6y2WLV+eKJI0qYym9Rfj3inv57Gu/t5qCTxPDSYP3Hx7gzLVN1qs2QwmNg0NxJKkzEre7eqFUd/ijv70M0JrXs/kqj+7PslyyKJnhXDk2mmQ2X+WTPcZ1a6MGQcBaxUJTJOKqjGl7lOruPW+3iRAhQoQIESLcH0RCRIQ3HfcjRu1O9rlbIaA9FrOZSDEQU7i4WOoglHshAnshD93Ebjhp8OBYiqWS1Yi/C+MiLy+bPDyVZiQV40gu2eq/rlihE30vMrjTONqJycxSkdfmi9g1C98HRZbIxMKkhhdmVsildB6ZynI4l+wg0u3EJNVolSjUbcbSMSzXZ7PqkNTC8vtukr6Xc3p8PMPUUIxizaFsuaQNlVOTafwg4IdXNjoqGXZDWnoJHjNLxZ6f6/auGE4aqLLEUsmibDoMJW+vfjfx1TNznBhPk4mpLRJarDt9yVg/NMmwKgssxwMhsFyfU5Ppe2LG1xRl/vBbl9io2AwmNaYG4shS+D3NOM3mdz19apQ/+c4sr1YsLNdrtFboLdGmHV89M0c2rpFLCTYasZ0CQIiOc9UuqDSPNxNTefdUhpmlMoW6ywenh/jthjD4RqFnhU4PcWoya/D6StiiUTIdbq5VGEjopHSZUt1hqVDnE21kfi+4X7GUvQSlsUycT75LJ5cy+j43u6sXmtGsXz0z13ouLBTq7B9KcHD4tvjiBwGXlop88fnZLfG4tuvj+QFxTcbzAjYdB0OVMDTBUtGM2iwiRIgQIUKEtwEiISLCm477EaO2133uRQho7vvH128nUgRBQMXyyMTUDkIJuyMCeyEPvVZdJUniM6f388LMKvsGQsf+lZLFy9c2OX1EMD2S4Ievr1G2PIQIqzgGE1prVX+v45hZKvKH37oUtijEdQYTGgldaRkjDiZ0Pnp8lLLpbjmP3aufjh8wkY0z0mgraRLZa2s1TjdK/O/knAKcGM/0NLP84PQQmZjKxcViS7hpRi3ulrzMLBX5k+/MstYg2K/ny1xYKPK5Txzt2V9/OV9h30CMiulyYiK1hVjnSyaPHxrs+I67iZX82pmbvHhlnYG4yiP7w9X37QSXvVTxHB/P8IVfO9G6Z5YKNV6+vonrBxwcCltiZFlqfZcfhKaezdaK5t+7kS+ZjCQ1JEkiroX/PM2tV1viWRPtgko7+a9YLh8+NrJl7G9kK1W/Cp2mONVtTul4Pposo0iCiu3tWA2wE+7k2dc8N5ocXiHLC7acp+38WLY7l81r2jEeXe5IfulnjHlrvc5kNt7xTI6rEu+aSHH2VhHb9VFlgecHVEyf//k/P7Wn6qEIESJEiBAhwpuHSIiI8KbjfsSo7XWfexECmvsumQ4pPbyFLNcnaSg7Esp+hGgv5OH4eIaPHs9t6bmezVc7juHkRJqXr21wYaHE8fEUrg+eH5CNh2OWxNYEhd2MoynarFcskrqM7fosFU3GMwZl08X1AwaTWt8EjO7Vz+cv5UlobSX9jXNaMd3W9883SrGblQ3TIwkGE/qOJL2dPJmOy8xSmc1aKEQcHU0wt1Fj30Acy3V7tthsh2fP3OT6WpWUoZA2VCzX5/palWfP3OTf/MZDLXJ8aanIrfU6R0eTHBhOsFq+LRA1xYiy6TKaDs/fvbgPjo9n+Le/8VDHfBtJqX1Xie+knL9d8JhZrjCS0tFkQd0JIx1/7yNHOD6e4YvPz3JgKNFKjYDO1op2jKYNSnWntWoOYGgythdsW8GyXa///WzT2guac/HaaqWjRSOpK6R0BV1TWsLbbj0iej1P9vLsaz83igQvX9sgAB4/PNCzGupOzB17XdPu5JdeIsflfIWjo0kcz+PH10uUTAdNliibDp98aIK4rnBuvkjFckloCvsHY5EIESFChAgRIryNEAkREd503I8Ytb3ucy9CQHPfmixhOh6iUfp+ciK9LaHUZdGXEO2VPLwws8qJ8TSPHxqkbLq8MLNK2XQ4Pp5ufS6XMnjvoQHOzRc5N19kKKnxoWPDPfvsm9jNOJqizXBSZ1MI1qs2Atio2lQsF1WWmM4lWp83HZeXr6+3yIsmi47zkzSUBlFRG5/3Wr+HcGXUcn1KZhi/WKjZPHehQkyTmMjGt02BaCfLP7q6wUBc5QPTg6iKzJe+d5VjY8m+LTY7EdVXGykczejCZmXMq7eKre9uEvHJbLynQHRiIuDSYiiOnBxPhb3vg/Gec3a3q/p3svrf3m7UTvqaosp23zG3UeORqUxHWX17S8le7q3PnN7f8g9I6TJly8P1Ap556iA1O7ijdIPnLuTxPJ9LSyUqpkvSUBhL6XfdqrBXNOdit3/IlZUqddttCW+wOwGqn8DSbGOCnZ997QLspaVS657rNhxtj+Lc6znrdU27k196iRz7BmIkDJlXbjbTZxRMx2OtYjO3XuWB0XTLmLL7mRUhQoQIESJEeOsjEiIivOm4HzFqe93nXoSA5r6fPXOTf2iUvr97KoOmhKXvzRYJ6CQCMVXqW3WxF+GkX/XGQqG+RQAxVIWPnxhrkcH2KoheZHA3/fzNfaky3Nqs4bg+CFDlsJz+1ESqld6xWjb5yfVNkobSIkv5koUfBBwYSpBqkMKlQp1jo0niuszL1zYRwPHxFMW6w+V8hVMTKfJlm2LdYbVs4QdQt33GUvquVu5zKYOPPDjScW5cP2CpaCJKomeLzU5ENSBotRo0UbddNusun/uLcy0RoJuINwWil69v8MMroTjyxJFBDFXh5noVx/VYKoZz770Hszx3Ic//8t1Z5jfDc7R/KNF3Vf9OV/8XCnVUmZYgIwlYKtS5slIB6PBa6P6O1+YKFGs2SUPpGb+6l3uruaLdXu2zU0rITri4WGR+o46uhskpluMxm69Qc7ydN74DbCcE9fIPAThzdSNspyrXW8LUk9ND24ps/Z4Ds/nqrp997XOzYrok9VBU6zYc3c2x9UOva/qfPTLObL7K9xv3ydHRRIehbdML4u8vr7TuTQAhBKNpncv5hqfGPRKuI0SIECFChAhvPCIhIsJbAvcjRm0v+9xrBcXx8Qz/pqv0PRO7Xfp+OJfcQgS+8uINhpKdt1zzRX8vwkm/FeZMTKFYv00g2o/huQv5XZPBnfr5J7Mxzt3a5Px8CU2WkCWB7Xp4fsCvv3uM8/Ml/uzHc5iuh+v5xHWF9x8ZvN2qMRjHcUOyv1CocyiX5BOnRltE5PThQQLCsvVMTGXfQIzj42nWKxb/3+wqARDXZHRF4lAu2VHZsZfWl8GEynrVRmustkJni81OpfGPTGV5+doGiFDI2KzZLJUsJjKxDhEgpkpbBKKa5WG5PnFVQVfl1rk5MJQgE1P57MeO8u3zC3zp765StRw2azZBAKtli8c9v7US3C2W3Onq/2Q21iJ9nh+wXLQQQEKXudBVIdJNgAcbSQhXVqodrSbNubXTvdXrmn39vzm97bnfCyEumS6IsGKlarls1GwqpkvJcrYl+neC3QhB3edDlWUODSfQZdEhTGmKvK2ItF2lyW6ffe0iUdJQQnNT2GI4uttjg95RnYdzSU4fHmKhUEeXBS9d22wl+lxfrfCNs/M8uj/bIbJ99HiOb77qMBBTCIKgFQ/6ngMDrFXs1vMjMqaMECFChAgR3p6IhIgIEehfQQFscW1vf+Hdi9ix08rwnZCH9v2cGM/w9KnRvmLGboSW5y7kd+znf/rUKM++dBNJCOJaSFx1RSaly/xgdp2K5eL5AbIA04eq6bJZtTtWy5eKLp/9WGdqQj9jvi8+P9uKo0zHVCazYbqG3lglbfff+OPnLrNRtbFdn9fzZc7PF/j9p48xmQ0NFJfLVougx1QJVZb6ttjsVBr/6dMHWCqabFRtyqZLqR6KDe89NNDhj+G4XodAdHOtyqu3CkgCBuIh+Ts7V+DR/VmGknrrWL70vatYnkfN9nA8EAR4vs/L1zcZSGg9PTLudPX/6VOjfPPVBQZiCsvFOlXbJfDD8TbjFJtzYEt8bC7B2ZsF1ipWz+jE7US2O6ng2Os2mZhCsWazWbVZr9oAKBLIQtxzr4jdeM30S9Z47kKekUxsS4tBPxHpXnjrtIsih4fj/OT6JgFwYiKsRmq/jrsRuXpFdf7rv55h30CMU5NZxjMGP5hdpWK6jQotleWyRUJXWC5ZHBxOdlR2PDk9xMXFEhXLI2konJxIoykyJydiW54fbze8kQaqESJEiBAhwlsRkRARIUID3ULAnZa534ve7e2w3QpzPzGjn8Fl9/F+99IyBJCKqUznEuRSxpbqgOPjGVRZoCsC2wvQFYlcSiOmSpxfKHFoONEiE/ObNaqWx2vzRR4YTbNWMbmwUML2fL74/OyuXr47jldXwhVuQq8FuE2+vnbmJnPrtVZFg+X6zK3X+NqZm3xgeohvnJ1vRYaW6g5Llstvv3+KufV6zxab3cR5/v7Tx1pk4tJiiYen0lvSMJaKbgfxXC5brdSQjapFzQ7Fhr+fXeWx/QMcalTTuH6A5XgokoQm+7h+2E4iRMCVlSrHx+UtpLN99R/Cn01/jZ2O5cnpIX56Y5Oi6aJKgrghEyAo1V0s12WhEO6jSYAdz+PKSpWS6eAFPnFV7hud2G9e3knU5F63OTGeIa7K/OTmJn4QRj4mdY1MXNtVC85esFs/jF7n4ysv3thT2sW98NbpThx5/PBgKzWj29z04mKRKysV6nZoSLteEayUzA6Rq1dU51rZ4vpaFVkKDWlXyxaDcZUrq1VyKYOK6Yb3ZKMdpP24f/fJg61n6TupDeOtYqAaIUKECBEivJmIhIgIEfrgTkjSdts1e7e/duYmL8ysIxA8MrX3l8478dToZ3B5OJfsWJnWZIkgCCjWbL5zsULKkENTvYl0x/72DyU6DCZrtsv1tRqO51Os2aiyIK4pDMQ1TKdOue6wUq63/B/ee2irK/9ujjcTVynVXY6OJhlK6h2rtv/86+e2mEcSBLx6q0AuZXREhjYjEmt2sG2LzW6uRfNzzcqNdrSvUK+VTS4tllgo1KiaMQbiKvmShSZLGIqgYrq8eqvAJ06N8v3La43WEYuYKmOoEhXLw3Z9UrrCWsXqSciaq/+m46ErEpbrEwQBmdjOj/pPnz7A2ZsF0oaKLIWtOa4fMJzUuLRY5sPHRoDbPiLX16okdRlNEtgODKd0fvfJg3siUncS3bvXbULCXiOmykxmDGwvLPOfziXuOia4G72qFG6uVVkuWx2+Id3naGapyNxGjdfmCgwmtZYIuF2Fw73y1tltJVa+aLJaNgGBHwRIQlCx3FZbE/SO6nQ8j5rjYzoeKV1ho2KTL1m4ftjy1W1WC7fvm52qad6uFQV3+m9LhAgRIkSI8E5CJEREiNAH2xGe7V6CdyJKdcfn8UNDpAyFufUqn/3zc+wbiHFyInPfXqZ3SkVo/v9Tk2l+dGWdzZqDLEHd9pCFy+V8mX/1jfNYXsBkNsYvPzjM//XDm6yVLWzPw3R8BGHJ+3rVplB3mMwaDCUNUrqCrQacu1Ui1SivbppZNse20zG3k6Xuc98kJgJB0LVdQEiouyNDoTMica8eJb2uf78V6vcezHYQd0ORuLVRZ75QZyCu4voBNTssPX90f5bZfJXJbAzb8bi6WqVmu7ie3zIFDYChpN5TwGmu/i+XLFbKJpbrIwso1t0d/RCOj2eYGoohS3CrUCemSIxndGQh2Kw5LQPB4+MZdFlQtVzWqxZJXeHhfRlG0rE9E6k7Ie57bUloktk//NYlNio2g0mtNQeLdeeuYoK70T0Hmm04j+7P9l35boqA42mdYs2mVHc4e7PA0dEksiz1XP3vnn+9BKB7TdQ3ajaW66PKErIQeEGA4/ls1OzWZ3pFdVpe+GxYq1hYbvhnx/WpWh5+EHSY1fZq7emFt3tFwZ0IcBEiRIgQIcI7DdLOH7lzCCFuCCF+JoR4TQjx08bvBoUQzwshXm/8HGj8Xggh/lchxBUhxHkhxKP3c2wRIuyEyWyMcldJe3sMZ7HudLwEzywVt91uMhvrWAnbqFpczoepBKXGyn77frrx7fML/Oa/P8P7/+0L/Pb/8WPO3drs+f29sFCoY7kur9wstFYmgyDgH66sM7NU5NJSkUuLRc7OFahYLlIjDMIL4Ohoks2qw4XFUuv7fny9wGBcRZYEltswtxQBQwmNIAjw/ICFgkm+WMcL4F/9yoOcmEjz1NFchwhxJy/fx8czfPZjR/ndJw8CYUn7F5+f5eBQjI2qzY21KldWytxYq7JRtXlkKsNkNsbcepWXrq3z3UvLvHRtnbn16h2R0CYJ6r7+AM88dYhMTGWpaJKJqTzz1CFm81XWKhYpQyGmKeRSBpIsMB0f2/UZTuqMpg1+6ViO/UMJFgp1nj41iixLHByKUbM8bC9AVSRySQ1Vlra01TTx9KlRJEkHHcJyAAAgAElEQVRiLK2jyRJpQyWhqa10ke3mCIRCxnsPDfGP3zXOgeEkfiBACJ6cHuogzheWyoymdY6NphhO6lxbqzXaN/Z2LZ8+NdqqavGDgOurFV69VWA8rfed293bNP/cFEp64fh4hi/82gnetS/DifF0RzXNdtvtFU3RozkHmm04B4eTLd+QZjtIE81nwsHhJI8dHAgFQ99nuWz1JNb95l/7OdrNZ3aDmaUiX3x+ls/9xTk2qjaGEprTekGALAkSWtgC1cRnTu+naoVCgu/7jZ8giQDL8VElQRCAkASSCFNrDuWS/ME/OsbB4WTHfdNeqdV9HM+eudk6l/3O61sZ2/0bESFChAgRIvyi4I2oiPilIAjW2v7+B8D3giD4IyHEHzT+/t8D/wh4oPHf48D/1vgZIcI9wV5XCPutcG8Xw3m8YRjZr3e7vQ/8ykoVXZHQFakVGdm+n3a0m8BJAjw/4Oxcgbgm901QaEd7KkJ7FN5AXOVrZ25ya70OAtKGwnLRxA8CZARCwLmFIglNxvb81kv/qxULVZH5L983zvOX8hRqoUGkJEkcGEqwXDIxHQ/bD/hXv/Ign3xoktl8tWMle7VscnGxhOMF/MtvnG/1pu/m2vRaEV2r2NQcD98PMB0PPwBVFuwfirFvIN7hEVGsOywW6nz85N5JaLuYtFYxubISCg1/+K1LfOHXTmwx0fvKizewXI+0oVK13EYCRmg8WTQd9g8lODUZeks0V+jbV/FHMwZeEKDLErm0wVhKZzZf7Wnu2b5d1fLwCTAUieWytav0jObczcTUVgtPse7w6dMHOo7fUCSWSyZ+AJoikdSUjvaN3aK79L6duMMezB530ZJwr1oZdnNMzX1+7i/O7bjy3b46Ppw0GE4a+EFI0nfy1Vgtm1xZrbJRsVvzr1eyyZ2U/nffY4oUimcpQ8YwQpNayw06WjN6RXVODcTwg7AywnZ9NFUiZSjk0gZ/8qmHb2/bYwz9juMHsysMJ3XKlkvaUJkeSfQ0b32r4l74e0SIECFChAhvd7wZrRm/Dny48eevAn9PKET8OvAfgiAIgJeEEFkhxHgQBEtvwhgjvMPQ/VJ9Y63CZ/98eduWiH7EZbsYzu22Oz6e6SgrL5kOKV1pRUZ276cd7SZwaxWLuCZhuUHLBHKnyoL2VIT2KLwDgzG+eylPTJWp2R6eH5byV0wXSZKYziVYLJrUTJd9g/HW/izXa8V7CgEbVRshoGrDZNYgoSnoikQ2pnE4l2yNofnybTpuy6H/6GiCl69tEACPH+7vHdEUki4tFbm4WEISMJmNMz2SYDhpYDX8EyqWR0wTxFQZTRZ88+wSjx7I8shUtpWakY6pHB1J9iX026FJHNcqJq/cLKArEoNxlY2K3XPck9kYr+fLbNZsNqoOihSWokkCHDdgs2rjB/EtKQXHxzPsH4xzJBfn2mqNkumEkZqGvCPhWinVKZkOhioRi6m7Ts/YjanpxcUithtWaWiywHV9ViwLQ5XuqLpgr8S9e5s7/a43ArtpI9lrq0lz/q2WTc7OhfNvIK6wXrFa8+9elP53iwAHhxNcyVewXA9JEshCkNAknjgy1LHdJx+abAkSAP/0P/yEufUa6Zja8i0p1GzWyua2vhntx9oOy3VZq9hoikzaUDAdj1duFjg2muxovXor440SxSJEiBAhQoS3Mu63EBEA3xVCBMC/D4Lgy8BoU1wIgmBJCNFcQpsEbrVtO9/4XSRERLhrdK9i92qJaC8H3q5yYjfEoR/h2U0ChCaLLZGh7SZwuiLhej66EvbpN7fTe2zXvorcHYU3kTGYzVfw/ICxtE6x7rJetbGdsPJBkwUJXUGTJWqe15AdaIwhrKpYLZtsVi0cz8cPQnJ9Y71GTJEZyxqocmdEYvPl++Xr6yQNhcmswbn5IjXbQ5MlLsyX+Mjx0dY16+6j9/0wCaNquaF3QdXmlZsO7zmQxXI9qrbHvoEYfhCEsZqWS9F0OXNtnX/80ASHcr09IvaC5vVvVrQYqozpeAwme6cwPH1qlAsLRV65WUOWwPGgYnsYisxAXKVYd/jhlQ0+OD20RcTQZcGZaxukDIWUHpKul69tcvrwYM+xNc9T3fZRZYFAsFyyQjInaM23XnMc4GtnbvJiI0HkiSOD6IrSYWoK4T4MTWGfobLZ8A2QJcFQUr9rInUvIinfSmje75tVi6WiyXrVRpUlfu8jR7Z8Bna3Ot6af6ud8284qbfm3704j90iwLsmM62xDSc1dEVGVyQC2FZQODGewXF9ZlcqVC0XTZYQAsYzsR39HXodx6XFsC0ICGN8G+LG5XyF//bDR3i74I0WxSJEiBAhQoS3Gu63EPGBIAgWG2LD80KIn2/zWdHjd93ecwghngGeAdi/f/+9GWWEdzzaX6q3a4kAdjRB24447CRidCdAzG/Wqdsuf32+hqHKJLUwMlGWJAYTKrbj8eUf1EjrCmXLIxOTEASsV208H2QJzt3aRJYk/CBAVeS+FR+fPn2gIwrvB7OrBMC+bAzbCxhIaMS0cLV9PG5QtT3KlksupVOx3LDtoWEmN5zU8YOAn97YpGb7YUWF4xEEhIKEFB7DyYk0miK3yHnzv4VCHVWGV+dCEcJQJLwg4OZGjbWKuaXMuikkzSyVwvOkK1iOT8V2ySX1xjWVMW2PxUKdkumiSKDJEqoisVIy+c6FZRRZImkoTOcSaMrW+MvdoHn91yphDKHpeFiuz8mJ3pUpx8czfO4TR/ln//dZinWHesOjY3IgRkyVqVge7zs0SCambiEmAWA7HsuWG/blCxH22fcZW/M8qbKgWHcBgSTBUrHeIKpKz7aWP/nObEu8GWgkbLw6V+Q9B7JbxJVmMocsSUxmY1humIgwkTX6jGrv5xbeGSXrzQqTL33vKq4feqiMpY0OcWevq+PNc7RRsRmIK635d2oyvSX2Eu78PHaLALmUwUOTGZbLFvsH4+iyYLFooikyQ0mlI6Z4Nl9tHUtcE1xdrZI2VCYzBtfXatQdj8ms0Wr1gt5tI73mw2bN4Ykjg5TqDufmi1Qsl4SmsH8wFhH7Png7J4xEiBAhQoR3Lu6rEBEEwWLj54oQ4pvA+4B8s+VCCDEOrDQ+Pg9MtW2+D1jssc8vA18GeOyxx/q9j0eIANx+Abu4WOT1fJlTk+ltWyJ201vdjzjAziJGc/vj4xm+fX6B87cKSJKEDJiOz3rFZjSts29Ax3J9frZQRJEFFdOhavusleuUTA+CULlTZYmzcwUemkgzPZbuW/Hxx89dZiJjULEcFgp10oZC2XRCImy7lOouw0mNtKHg+QGeDx9qM5Z8cTbP5ZUq/+cPw+P7zOn9PHV0hE9/5cdAQCauMqYaLBTqoUdDY5X8tVtFkrpMum1FEzo9K+KajOv5CMJ2iisrVY6Pd4oEFxeLlOoOFxeL+I3xNYl51lCoWG5olElAyXSQCAWRqu0xoIRl5Ctli/2DMSzb5eVrG+wfivP7Tx/b85zqlcIwkTG4slrlx9c3GUxqrYSKdgIwnDI4NZHh9dUKluNyfa0a9swrElMDBhVL2/JdK2ULRZGwbZcgCAgEKIrMStnqObamwFN3fHRFbiUbVEyXd01mONHHP+DVSrg/xwtI6jJChLrwlZUq7zs02CGutCdzlEwHufHZhU2TLz4/e1ckZzetIW8W7pTMzearvP/IUMeqfrHubHmm7PYY2+ffesViOKn39Ri5m9L/XiJA2XJ5IJfA8gJeX60yltI75tFGxeJLf3eV9x8eaj0Dv3NxnZGkylLZZqloYrsewwmV9arDA43v6tc20us4npweomq5XFurMZzUmczGKJkuhdrOyTC/iHi7J4xEiBAhQoR3Lu6bECGESABSEATlxp8/DvxPwLeAzwB/1Pj5V41NvgX8d0KIPyM0qSxG/hAR7gbtL2AP78vwk+ubnLm6QUyVerZETGZjO/ZWbxeb98XnZ/E8n0tLJSqmS9JQtjUI/OqZOQYSt1/k5zdrmI5LyXQRQuAHAZs1B00RDCV0sjGPmXwNCD0GYqrA9gPqjssPr20QiIDHDg5vqfiwXY+59RobVZunjuYomy63Nmr4QYAXQC6po0pSaPZou+zLxhhO6WiKjB8EnL+1yUy+wmjaYDihUbY8nn3pFvsG4uRSOpokMLTwURL6HNi4bsBq2SSX1CmZ4TG1k4R2z4psTGWhaEIQMJHVWatYHau3M0tF5jfrmLaL4/m4PtBoA/GCgBvrNQ7lkjwwkiSuyfzo6jqBECiSQBYB61WHuKYAAX4QbtNsTbmbF/EHcgn+oRi2pqyUTAxVRpZgPK23VoZfmFnF83yWSyZLRZOfL5VQBJQtD0kKj0EWgh9eXeeD08NbvqNkuhiqzGj69pws1p3W/O1GU+AZTKhsVB0MCYJAxg9guWTxqdEEf/r9qxBAKqYynUuQSxktz49Uw0/CUMOy+5LpbCnpDwlqjePj6Q6vj0cPpO+a5MwsFXlhZpUT4+mWWWZ3a0i/7bYTCe52RfhuyNz9iGpspoC0Vzj18hi5m/ndLQJoskASAlWRGUwqvDq3SanmkDSUlmC5XDJxPL9DnKiYDqtln30DMfZlJW6sVylbPvnS7ePfKX61+1p+9s/PAbTaMiBM99lrfOwvAu6FcWmECBEiRIhwP3A/KyJGgW82VtYU4P8JguA5IcRPgK8LIX4XmAM+1fj83wC/AlwBasDv3MexRfgFQPsLWCam8vhhwcXFEmXTQZNljo4mO2L8fuu9+3juQr5vb/VOZOTiYpH5jTq6KpHU5R0NAtt9HyDsd1ZlCbvxYr1RtdFkgeMFOJ7HStlGALIABFSdAAEoAtwAXrq2iRBiS8XHldUqSX1r6oXvw2rZIl8yMVSJtKGgqzJ//KmHWudvoVDn9dUqo2mDkQbZyMTC1N+vnpnjkaksL1/bACHw/LBE3/FAkcOKjcWCSSYeCkHdL74pQ+HmRh1FFgwnVAxVwXR84qpMTJX4yos30GTBz+aLVC2XtYqF36gEoXEOQsd+wbv3ZbC8gHfty7JWsVktW1iuh+n4gEBXQhLl+gGP7s8ylNRZKpp3RFDb58EHpgf5u5lVqpbLweEED09lWyvT//vfX8N0PfJli5gikUvpeL7PUtEiAFQgoSvoSmgUOr+5lZg22yBMx8P1fNYqNnXHw3K9nqu/7QKPlg7FpaodCjFrFYsvfe8qXhC20liOx9m5Ao/uz7Y8P6ZzCc7OFQAIggBNlraU9LdXLVxeLqErEo8dGGAkdZtI3inJ6UWaNqthIsn+wXhfkaH9vry+WuGzf7bM1FCME+MZjo4meGFm9a5WhO+GzN0v34t7ZXi43T3QLgJ88flZNEVuHcdw49l5ZbXaEiI2qg5Dic7KHj8Ax/NbaT0jKYO59Rp122+1eu2mbaR9nJbrEW+0NSUNhZMTYSTr2yU1443E/RDCIvziImrziRAhwr3EfRMigiC4Bjzc4/frwC/3+H0A/LP7NZ4Iv3jofgHLpQyeOhoS0N998mDfF/h+vdXdZMTxPK6tVvjnXz/Hx0+MkS+aIGi9cBuqjOX6fVevR9MGpbrTIva6ImHZAlkC0/EwndAgUhJhv78mSyiSwPPDlX24baKiiDCO8+xcgUNDCa6tVnH8gH0DMeq2S1JXSBvtpeE2xbrDSFqnannUHQ/bDZhO6R0kBOBD/+77DHeRi5QuM79RYzqXoGK7VCwXy/XQFBnD80noCl4j2jGlK+wfSnRUlXz5B9c5MpzA80JlIQgCjuQSVKzQhyKsxghbKPIlkwNDMQo1geU2DlqEBMf1A9K61Ir9LNYdHp7K8MrNAptVG0US1BviyNRg2JN+ZbWKpoSJGneyyt05D1SGkjppQ2GjZnN2rkDaUBmMK1xbq5LQZQxFAgS3NusQhMOPqaEwUnM8VFli/6BBydo6T06MZ9gom1xaLmO6PoqAwYRK2lD7tv00TUkDYDilE9NkDFWmWLdBgOsGlFyHTFxDl0Nxrun5oSky757KMLNUplB3+eD0EL99+sAW4t+sWijXHVRZcG2txkBCYzhp3BXJ6b5n1yomP18u4/nw+KHBnteoO85ydqUCAoq1UGD80veucmwsuWsRodeL9t2Qufvpe3G3VQ97qfToPgfTIwlefH2NfNGkXHfQFAnX8xjPpFqxtiXToWK5CMJIXV2RkCXBQEJFlgRLRXNXAsq3zy+0fDYGE2orSvTxw4MtEaTZlhKhE+80A9gIbx6iNp8IESLca7wZ8Z0RIrwh2O4FrN8L/HarjF958UbrRbwZ3ajJAoLwJXi1YmE0WiKaJcNBEJCJ9b7NPnN6P3/0t5eBkNirksAj4ORECklI+A3SevrQAD+6th5WCmhhu0W7W6EPZAwFz/cpWz5XVqs4no8uC9bKJjU7NLP78L7bx1uzPRRZYjChM5gIf1esOwRiq2dst2ACsFYN0xI0ReaXjuWYWSpzZaXC5ICOwECWBIYqEwQBZcvtePF97kI+bFcoW9ieh+n6SAiWSlbLRDITU7m0VCJpKFQshbWKE7YNuB6uH4o2BAGO67Nsezx/aZknDg9hewFTg3Ee2R9WYHi+z1jaaCR6hEkgG5VQhImr0h2tcjf9KpqrsbbrsVYJ40v3ZWOYjseZa2V0RcLxAmKqhBAC1wpCQ1EJ/EAwmNBwvQBZFniB6Gi/aCKuCWbyFfwgwJAFPrBRdXlgRO6Z0AF0mJJeWiziNVQrSQhKDbNMgoCq7WF7YRXOM08d4nAuLG2vWC4fPjbSd6Wrnfg3Wzl0RXBlpcpw0rgrktN9z15ZqSKEYDCp9jU27DCiXb3dllS2XDIxFdcPWCqaHdGO/USEfi/acVWibLp3RObuV1Tj3axMNrd9/lIeVRacnEgjCXXbe6D72gQBeD6oiiBoPJBG0gZLRZO1BZukLqNJzXYOCd8PnwVpQ+XdU1kODif57MeO7jjWb59f4H/8q0u4vk9CUxAiwPUCbNfn4mKJp47qb3tT0/uJd5oBbIQ3D1GbT4QIEe41IiEiwjsW3S9gc+tVLucr7BuI9TXVm1kq8rUzN3n1VgGBQJdvE/P2F/GmDwNAKqaQiamMpg0KNZvVikXVctFliZgmM79Z7/l9n3xoEuC2MV8mxj95fIqaHcZKDic1Li2WeH2liucHVEwXTZWZyKjMF8zWflQZAgLqToAkIKZIxNRQCKnZXkgRggBVllul0JIkiKlSa5WyXTTpJji//OAwz74UJuumdJmy5bFesXlkKtN6KRlN3yZkJyfSfcv7Z5aK/NVr86F/gSqRS+qkDBXL8UkbCrYX4Acul5ZKXF4uE9fCNpfVis1YzGAtaBAgL8AnNK1UJJCB2XyFgYTKmKvjePDAaIqxlM6hXLK1QrtWsRhK6jzz1CG+8uINBpOdj8DtVrlnloo8e+YmP7mxgSJJjKV1LMdjpWzh+aHJYxOu7zOS1CiaYaWJpjQrWQKGkxrrVQfL8VFlqJgeshAdkY5NfO/na4ymDZaL4fXWJIGsCq6t13jkwGDPsbYT33zZYjSlM5zUePn6JhC29pQsj7gmGE3pxLQwovOZ3O6IYTvxb7Zy6LJoVdncDcnpvmfXKhaqJDGdS7Q+032N2u/LiumGbVGu36oAGkyorFftju/pJyL0e9G2XY9i3Wl9/17JXD/h807FhLtZmWzf1g98CKRWi44Q8Hq+Qr5hhnp0NNFKwGimZBwYSpAyFC4ultAUiQ8dG2Y4ebsq4ac3wrhZxwu9WE4fHmQ2X0GSBA+Pp5lZKvPqrSJJXdnRXHJmqciX/u4qjueT0GQ832ej6jOYUJEliYVCnW+dW7xjU9NfhDLz+yWERfjFQ9TmEyFChHsN+fOf//ybPYY7xpe//OXPP/PMM2/2MCK8RZFLGRwYijG/WWdmucS11RpHR5NMj6YomS4/urrOgaFYq7R3ZqnIHz93mUuLJWKqhCTg2lqNmaVyuN1Ikh9dXQfg6moZRYjQWBK4slqhZjksly1G0wbZmMJ61aHmeLxrIo2qyFu+D+DoaJpPPTbF73zgEJ96bIrHDg5x+sgQU4Mxzs2XGE3reH5AzXYoWx7phuhRqjt4Tb+EAGpO2K6hypA0FAxVRpUFMU1hXzaG4wc8PDXAYtEkl9IZTekMJTRM16dsudRtl42azY31Gt96bYlSzaJsupybLzCbr/LhY0MUzZB0DyV1ckmNRw8OttIVAOK6xGy+yvRIiuGkxlrFplB3ee+BAX7nyYNA2PZyfbWKECCL0DQ0oSv4AEKQS2r8+PomsiRwPJ+a5bFRC1MwhCQwG60MCPD9sPUjbSgISWI42YwZDc3yckmNzZqDrspk4xrpmEo2rvG5jx9teHqUWmaQTZTMMK709JGhjrnUJG+X8+WWCWjZcklqMmUrnAOTA3EsNyCuK8RVmZrtk9Rl1ipWqwohpioMpwweGEk0KkU8UjGFf/krD7aEqXb86fevkNDC8+T7YSyqJkuYbsDhXLLvWJukw/MDxjMGq5XQX6TueNRsD1kSaIpM3fF5YnqIuKYwv1knG1d49qU5/vLsPBcXS2TjSsd8BTrOW0JXyMQUbm3WWa/abNZspgZiPDyVZa1i7biv7e7ZxaKJ4wUcGIwzORDve42ycaV1XxbrNiXTxfMDTk2miWsKrudTqDuhsaoSnsumiNA9nr88O89QUuuY15oiUay7/NMPHmqNSxCgyIKf3izs+ti60ZxTAENJreczqR+efWkOCIUSIURrDs9v1rfMh+22XavYuH6AIgnyJZP5TRPL9RlKaLiez//7ygIxVWLfQBzXDwWcmBqej3zJ4j0Hs+TavEE0ReJnC0U+fnKM6ZEkUwNxhpI6mbjC1dUKcxsmMVXmPQeyaIqy4/E++9IcP18uIQmBH4AiS0BA3fEp1F0ODMX5+MmxRrRveVfn7l6c/7cbcimD00eG+PjJMU4fGXrHHV+ENwZ7+TczQoQIEZr4whe+sPT5z3/+y73+XyRERHhHI5cyyMYVnrsYlpwHQEwLWxI2qxb/6fwyL1/f4OJiiTNX15nbqKEpEoamoMphP3PNDtsBfvXhyRZJur5WCxMcPJ+YphDXZJZKFoEfMJTUWC5Z6IrEcFIDIZGJKczmy3zj7AJ/c2GZ715a5upKtS+BaZKFsUyMqcE4pyazaDKs1xzWqzYJXSFthGZtfmObmBI6ODZLoYUA2wtIGQqyJPOn/9WjrRfRiazB2bki+wbiyAJmlsv4PhhKaJa5ULCAgJGUgeX63NoweeapQwDc3KgxX6iztFknm1CpWi7nF4pcXQlNMYeTGo4PD+3L8tmPPcBvve8AuZTBF5+fZTZfJl82sZyQDKtyeH4NVWI0rZPUQlKrKxJ+4LNRcQiCgKSuMJDQqVke7z2QRVNlLMdHCBokP6yMWG20SDy6fwDPh4XNGjfXa/xsoUjd8fiNRyZ4YjoHdBLYnQhq83rMbdTIxFTiukzFdFku1bFdH8eHXErn4X0Zjo2lKddDUSeb0BhKqDgeOH5AJqbyrskMQymdmu2BEHzogWE+dGyk5zz4xivz3NwIz4frBwRBGPNqKBJjmdiWsXaTK8/zefVWgZLpMJzUkISgULORBXgBeH6AIkmkYzI3N2r8bKHU2rYfMWuet42Kxc+XS1xcLLFWsXloMs3pI8N4PvzNz5Y4c3UDXZV3JHnfPr/A//AfL/Kn37/C315Y5sBgjP/6iUN8/OQYpybTvHqruO01ahcvSmaYKHJsNMlYJox1tL2Af/K+fVQtryXE9VsR3u5F+1cfnmyJhC9d3yShK3dFYHuJCd3PpH7Ph6ZgcmWlzPcvr/DjGxvc2qhRMR3+i8emtny+17bhd0rc2qwjC1gqmo10mbCqaW6zTtDwYZkajIdJKqrMYELnD3/1JCtlC9dny7mqOx6ZmNrxe8+HmuPzvkODPDiWJqGruxJP/vLsPI7rE0DYkkbYYlSou+iKxAemh0jqKhXL4fJymb+5sMxq2dqVMHQ3Yk6ECL+I2Mu/mREiRIjQxHZCRNSaEeEdjSYx26jYDMZVTMfjlZsFDg/Hubpa7TDBe/HKOrIUxlk20YwwbJYeNkusnz41ymf//ByGprRaG1zfZzxjENcUcim9kegAa2WTUt2hatrkyzbrDRNFx/WZ26h1lFM3V7P/42sLraqF9ZrTKjk/MZ4mG9dQZfjuxRUGExqSCBM2LC9AFlC1QtPIgABZCCqWx+nDg1vKkD96PMdsvspfny9gKDLj2bC/OwAkCQo1h4lsnLShsFQ0+XfPXcb1Ahzfx3Y8rq1VWSnXycT1VnTl4VyCuuO3jmlmqcgXn5/l4mKRn9zYZDSlkTZUarbbMMikYWoZGnICPH54gGurNRYKHkldRpYlbC8gHVMZTWksl218P6DueAgREhNVEiwUQ4+O4aSOJASOF3o3pAyFX3t4YksU5F5KlpslqWlDZaMaxouW6g4+EFfD8d1Yq1E2XR6azLBcsnn0QJa641MyHaZHkoyldZK6QgC8eGWdgXiYuqEqct+y+n0DMa6vVxFI6HJ4Lb0A/CDgo8dzWz7f3VpwKJekVLf56VyBYs0lHQvnZsUKhSBdCdtzXr62SVyTeexgfMf+32ZqRrNk3vXDFfR82Wa9YpFLGWw0WiFOTWa23de3zy/wR397mYSuMJLUKNUd/vVfz/DX55ZIGGrHPN3uGrW3PnTP8+bnP9n3KXEbu+mnv1d90ndizNnEZDbGubkNzt4qoslSowLH49JimW+fX+hZXdO+bbOVZThp8J4DWS4slHC8sD1qeiRJLmXw2q0iKV2mZDqtbdvLsPudq8+c3s8LM6tbfp+JKaSM3bdCNcfquB4l02U4qVE23YagHPD4oQFyKaOnX89u2lSiMvMIEfaGqM0nQoQI9xqREBHhHY0maRhMaliO11r1em2+GKYbtJngDcRV5jdr1CwXLwhFiKQetjl095MfH8+wbyDWYVqYS+oUTYeVihWmRngBMU3GdMbG6X0AACAASURBVH1kSbBScZAlSDaiNX+2UOLJ6aEWgWnv3R5N6ayUQmIyljbIxlVKpkvJdBlN6VxYLOEHYUl1xXKRJdHwVwAIWK1YaDJMDSbYP5jgiemhLRGH33ptASHCF/dMgyDoikTFdFFEuD8IY0U9P6BQd5CVkBAnDQUhPIqmi+tbnJjIMD2SaEVXPnvmZgfh9nwfVRKslG2GkyqWK6HJYTVD2lBJaCpjKZ3ZfIW4KvP+w0MdMaS6KnP68BB+EDCzVCJfNFFkCc8PEEFAyXJxfXBcH7XhqXmlUaHheEFfo8Pdpg40ydtQQmVmKSRtjfAObC9gNKVjewHFetieMzUU48GxNFJbib8fBK2UgI88ONJhfNg9riYShsoHjgxxdq5A1fHRVInRlI4kSR2iShO9yO1SyWIgrpHQFBCwWKgRXlqJwUQ4BgFULGfXRHE2X+X9h4fIxFS+e2kZSYRRsN+9lOdwLknJdFoeKtvt66tn5hrtHeE4VNmnaoWxor/53imKdSf0r9iDK3svUeIrL97YlQfAbl607wWBnVkqMrdR49W5TYaTOtMjiV0Zczbx9KlRnn3pZmjAqoT3gSwJBuIqXz0zt60Q0S0gqLLM4VySkxPpjnjOpKE0TGp7G3Rud66axqftv3/uQp4baxWWSxYl0yFtqIyl9Q4T0d5jrXFsNMlS0QxTcmIqh4bijGbCcfTy6+l33trxVkmT+EXwqYjwzsHdJvVEiBAhQjsiISLCOxpN0tA01QPQZEG57pDW1Q4TvImMzs+XS+iKjKFK1G2PYs3hxESap0+Nbtn3yYlM60V2tWzyo2I9LJtXJZKazGKhTjauoUkiTKQIggaBDwl/3fFYKpqoSiiOtK+0PjCa5OpqFUmEQkNMk0NjSVnih1fXWK3YuJ6H44LU4LqC22EafgC2B74f8BuPTjCbr5KJqTiex/d/vsGN9Sq2GxrAqXI4PscLSMcUXN/H8kGRBBtVC1kK/TLcICAmi0afNiR0hbrjoSkS7z98u5TZcl1+eGWDgbjKQCMx5NZmneGExkrJCo9Zlqjaoe/BeNbgXZOZVmnn5XyFgURYUVIyXUzbJWmoHQ7/2bjGqX1pXnx9jYXNMDZVJvSROL9QIh1TKZkOmhSKJk3c6Ypnk7wtFEzG0jpzG3UCQG/4cHjAgaE4JdNh/2C8J8mZW6+yVLL4yY0NRlM6D4wmWyZ//cY1mY1R1BUO1d2WkGY2fvZKzeiXOjGejTGdS3BltcrCpsBQYCIbw/MhrsocH09xfr6062SIdjIuC8HCZh21YexqOR6bVYeRlN6xTa995UsmI8nb0bAbVRtDFWGSyg5kfCfcqaHjTi/ad0tgm+MaS+mUGhGjL76+xmbNRiBQJMFq2SSX6h+Fenw8g9ZIjrE9H02RyKV0YqpEvmT2+Nbb3/3chTzlRpVXJqZwYjzTqvhoFyjGUjpLhTrHRpMtk9vu6pDt0oe6K1++fX6R6+s1NFkwltYJAlgs1Pn4ya3P1vb9NMUOVZE5fWS49SxujrVYt9HlMML35ES6Nf6d7vO3QppEFIcYIUKECBF+kREJERHe1thpNalJGnIpg0f3Z7myWmWjYpOKqRwdTXb0NS4WLQ4MJlCV8GVekgQT2RjTI8meL4XtL7JXVioYmsIgkIqpeD5MDsRQZAk/CCg2CF5TNPD8gJgqs161OX1kGOgkd8NJg0xMoW57VG2XQV9DALoqsVm3qVleS3QIGjGfgjARQW5E5iX00EPihZlVyqZDLqXx6lyRzaqNH4RVAjXHI5fUWSqalMwwZlOEFc64fsD8Zp0TY0kgrMhojw31GiZ3ftvvAC4tlhmIq6Frvi6H/deKzGbVRpIFtuNjqI0xxuQOEeLAcIJao8c8E1dZKZm4fnhckggJX75kMZrW0RQZL4CBhNYSjqq2i+V6fP/yari6H4SRgs9fypNskKtDuf4rsP3QJET/4uvnkSTBYFLDtD1UWSBLoa+G5froityah92JLWfnCjwylYUgoGS6vHKzwHsOZLeNvGzuZ6NiMxBXMJ0wivXUZLon2doudSKXMsLzHASsV20+8uBtAlisOzwyldl1MkQ7GTdtl5rj8f+z96Yxlp33md/vPfvdb+1VvW9qspvURllDt0XLthZLMwJmMoNZPmQGBmLABhIogGcmToBBYPiDg8nAgT44ARIFHkSQkkw8GXvgGY0pkZIdmlKLnhFp0s3e2Oylurv25e73rO+bD++5p++tvtVd3SyKFHkegCCreO/Z7rmn3uf/f/7PQ0Tq+RFTckyiRNLsRw/c1s5o2CCWoKA0lEDyuMWjdytq7p0S2OHjKnsWr99pstUNSZTiYE1H3w5SLBzrfjXWAIcni/cpFpr9aGwMLIyS3jML1ey4h5+ZwwqH4zNlvvT03EPHYh6GwfhNP0youtpo9c62z5HJAp88XOfqaveBYzO7FTsGx2oIA4TgmSP3niN7KQy9H2TmeRxijhwauTIoR44PJ/JCRI6fWuylmzRMGqbKmrw2+xFfODPDi5fWR4jSdi/i505OjkRRDuT04zAuJnFALIff+6vPHeM3/tXr+FFMO0hIpEQqRdG1sU0j6/Dt7LROVzRJO5L+bBraaHCjE2IbglgqJPdqAwqwTE3wrTRdYUDqr662+as7TcJEE+Y4kVimgcDAj7VZZDeU2bl5ljZv64UJb9xtM1918aOYjq+VEpYpMA1B2TUpuXZ2HRc3u9zc7DFRsIikohvo4wzSOe+SbTJVcpmpuNzZ7lP1LK6td0cIxFMHalmM5D/7wzf4Tze3uZt+BjXP4s52jxsbHRzTZLsTUC/axInUrvpCIFHEaaFnpeVTci2mU/+B5UafL41Rt+wFZxZqfPHsXKoeSfjBWxs0/BhLJhhC0PZjjk+XsgXUMMlZbgV88nCd4zNlyp6VqXNeX2xgmgbbvYjnTk3dF2c42M5v/fFFNjsB02WXpw9WsxGYcSNDg/1eXNYGnQGSa+vd7DXb6T30Z1fWOHuggmtZNPtRZka6F2J2r0ASsNbR3hxBnCAQbHUjPn2sTiw1sXrQtn7l3BH++Z9cAXQ0LAr8WPLpY5PZax5XLv9ueQC8UwI7fFwzFY+S2+XEdIlWEGOmaiPXFLy51OLETHnXAsfOa9cOErpBPDYGFvZGeseR/r14azwI3zi/SCIljX6IVPo55lkG/VhydLr02J/HsF/P11+6gWOZuyo3HraN9wq5T0WOHLkyKEeODzPyQkSO9wUepxq+14X1ONIAULi2ySs3NhEIPnm4xnOnpnAsc2QfDyNBwwvZG+sd3lrr8tpiE9MAP0qwTIPnL6zyt59Z4I9eW6YX6nhJqUD0Y37ts4fGFk12SqOvrLZxTYO1doABOLaBmUhCqZC6iYxtgECgFBQcM1UkWPhRzGY3oBvEFB2DAEWcgEJRL1i0/CiN0wQh9HVUSvs3lFOC0+hFJBIkEEpFIhVF12S26nF4osgrNzYJogSpFFXPpB8ltP2YfiQpOyaeoxNI/DihKrRj/rPHJ3g7VajsRiDWOgEIXaRp+yF3Gr4uohiCetEmlIpGL2Ky7CJVgmvZJFJpL4VaAcMQBLGkE+qCzBNz5Yd2YB+EwWdUK9icOzXFf7q5zWorYLpsc+7EJP/w3NGxpO6f/uvXsU340fVNWn6EZQj8KOZ2M+DUbJmfOzmJs4tp5ZmFGr/9N89m+614Fjc3OlxZ7VAvWPz9/22Tqmfx1IHayPdmcavHp49OcHmlTasf8aeX11KjP8FcxaYfJvzg2hY/f2pqZJ97WfgNvle/+a/fIIglAkHFs5mr6o7+cjPgF5+YzQpKu2HgZfCN84ustnwWah5u+tk9KqnciXfTA+CdENidx9XxYywDZite5hXR7IcYwnjgQnzntZurenz1cyd39Yd4r0jvjfUOjZ42LzUEKKXohAkJ+/N5vB+UDY+L94tPRY4c7yVyZVCOHB9e5IWIHO85HrcaPlhYb3R8rq11WW/7OsoRPf8wIGU7ScPw/r5wZi4jO0/Ol/ijV5eJpWKyZDNf9TBNY08k6PRciT989Q4l18IUipsbPgrFZ05O0exHLG5F/O1P6mJEJdEpAws1j8sr3awLvnNBPSyNfmutC0JgCB2XpxQYhsABMAX9SBJLEEJSciykhDCR/LXjE1xabjNX8ZguKdbbAbapiE0FShFECVGatmEZgjg1XFSpzEIpiSm0ssIyBU5qvigVTJYcOn7MXK3AqbkKL11dZ7nRpxfGqXGm3m4v0hGjEwUbyxQoyJz4F2oevUhmJo47CcRyw2ezExJLqUm0hASQiR5vqKTjJ45l0A9jQqmLHzO2y/X1DrNlB882+eWz8+lxq3dEvEY/o5i/8dED2X32IGNE1xScv75FxbMyA86NjlY0/OITsyP7GLf42ql0uL3ZZ77qsNwKEELQ6kUUbZOvv6RTWL51/hbX1zvaP8A06IUxm90Q0xCcnCliGjrp5ROHakxXvMde7DVT89S1dkizH9EJYmbSyNrTcyW+9sLVhxYXv/KxgyPkebfUi0fFcGEviGMuLrV3VZ68m/j2G3dHigWff3Kaxa17IzC2qdU0Hz1UY7rsZWoXPUIx/hiHr9G5E1N7Kty+V6RXp9voZJswkQzsW8M42TdPhsG5D67J8xdWR37/fsX7waciR473GrkyKEeODy/yQkSO9xyPWw0/WC9knWGlNIFOpJb+3ljvZKTsYRGHtYLNVifgj15b5ol57c6+2Q1p+VrmvJfF7NXVLs8cqbPSCnh7vUPR0WaC2/2YJxf0fr53eSNLGhig2Y/2JI0eLFiXm32CKMFPkyxMIVBK4VlCR5Ju9Gn5MbYpmC47XF3tstb2+bmTk0yVXX58q4FrGURxwp2GTxBrdUHZtSg6BkvNAJUqH0wBYQyGoRUcUipioGALLARRmgoyOJ9mP6TpRxhCUPUsttOChmUKTkyXODxZ4OW3NkAI6p7FtbUO/SjhF09P86vPHbvvvC8tN1nvBMRSG/Il8t4YyoDMBLGi6mliH8R6JGOh5jFX9bi52eVOw+fIZDHb5n51YMcd627FNNApLWttn25gMlNxMQ2DWMrM4HGAimdxMY083UngB/987YWrHKwXubTcwrPNzMBypR1wdqHK733vrTSKVlCwTUquSaMf41oGrqVHaQZYafk49qgKaK94/sJqao4YUXRMwkQSxpLVdsCnj03w4qX1x5La7pdcflC8+db5W5l56oOUJ+8GxsWTfutHt/mHP3uYXqiLYk8dqLLaCrDNvY0WPG7h9r0ivY4pCGOtwBoUOwVgGezbZ/DTKu3+aVZzfBiQ+xb8ZJArg3Lk+PAiL0TkeM/xKNXw4YWBYwpev9PEs03afoRAYBowWbIzUjZM8gfv/bd/eZe5isup2XtmlSstn04QsdIKaAcx02WX+ap7n4x/5/4FECSKi0stPn64OhI7CVp2PTif1ZbPs8cnh09n7HkO78NNFQRholhv9emHMZ1AZqMUUimEEDy5UMGPFMenS4RxzFYvptGLOTblsFD1eGu1y2RJe1hcW+vSCWKeXKhSK1hMlRz+8rY2sSw5Jt0gAUgjHkFKLamOJYCiFyiEodMRPnFkIjvuKFEopUAIXNukGEvCRGKbBrFSbHYjraIIY+42A1xLcLDmcW2ty2/8P69zaKIwMl7w/IVV5qseW92A5WY07JOZmWmiFF7R4edOTo+oDnphTJwoukHMUqPPWrufeSG8G8Rrt2Lat87fohdJgkhyZKLAZjfi9nafI5NFjk4WCWKVKXpafkSSKKJEcrBe3JVQDb4vw/fZIHY1iGN+8PYmjqmTThIp2epKwjghlpKioV/fDfS4TsdPQAguLTez89jrovtuo49lCBDapNIQ2pg1jBWXV9pMFt33XGp7ZkErPvYal7rf2BlPOjDl/N7lDf7g189lr3sUFcjjFm73g/Q+DjE7NVfl9maHTpAQJoqCY1B2TQ5PjTcBfhz8NEu732ufihzj8dNa3PppRK4MypHjw4u8EJHjPcdeq+E7FwbagyCh5Jp0w4SSYzFZcig6ZkbKXri4lRUNVlsBhyeLzFVcWn7Mq4sNTkwX2exGXFhqIaUea6gVbPwo4fJKW6cBjNm/ZcAr17dQwLMnJrBNwSvXtzl3UlD19PuBLDqy7cfMVb2HxiMO78M24fz1LQQwX3V4dbFBotIOY6KIFDgGzFZc7mz7mEKrIFp+wtFUBeDZJqdmS5x/e4s3l1p89vQMZxbMzJzw+QurNPsRzxyp892LqxgCDONe8WGQyDFIxhBoHwlDQQwUbSM7dtcyGGgVlALbEPRDhZSS9XbA3e0eIBACPMtgtuohleL2Vp8D9QKtfjSy2NOE2+XGRhfbNEikHClGRGmlZK3l89qtLWIFZcekG8SstQMKtsnRyQLb/XisF8Lgej+IWO2FeF1abvLCxVWkktQKDqdmS0yXdfTit99Yo+LZNPohphCpGkIbgc5XXd640+T821uUXRPHENxq+pRskyhJMIQ9llANvi+D+8yzTYJYUvYsLi61EQjmai63t/okUitJEikRhvYO2e6GbHZD7aVhCRaqLr/7natIpTg6Vdrzots1BUtNHyUV235MmN4kJtDqR1xd7VD2rKzY915JbX/Sst/he+bKSptD9dEY04pr3hex+Shk9J2cz25qnr0UF/ZKzHZu7/NPTvMvX+7gWCaude958ivnjuzpfPeCu41+6sHSouXr78aJmSJ3G/G+7SPHhws/zcWtnzbkyqAcOT68yAsROd5z7LUaPm5hMJ9G1T05X81ImR8lmAa8cn2bimexUPN46eo6bT9mvubykbkyP77VIIgTXr62gW3q91gGrDR9HMug6Gi5f7Mfj93/xeVWVmS4vt7jqQNVXrm+xYW7Lc4eqPDK9W3CWFIrWHz7jWUsQ/C3n1ng8kp31/P89ht3+Z3/cJl2P6ZasPAsg4qnz/XVxQax1JGZErAExApCCWvtANDGlHcbmuDoooD2Ypguezx7YoLXb7fGejEMyMWJ6RLX17uYhjacNA0dxdgLZRYPaqUqEENA1bVYaYUcSxMzKp5NvRgTRJJ+lGAaAtvSXXLHFLR9iVKKWEIbWO+EGIBlGrT6IWGcjCz2DtYL/MlfNbBNQTCaHDry37GC5aY2OkwUbPci5qseEyUHP0qYrhaYr7i8td4d8W8YPvdxxOphxOvScpNvnr/Fy9e0UWetoOM1B7GcHT9moxNScS3mKi53Gz53Gn0OVF06QcyBeoEn5yssNX1tLOpZTKRmlNfWukyXPdbbPtfWOqy2g+y7Mvi+zFddLq+0s8jLo5NF/vJOk4N1jzDW1zqMZapkAc80ODZZ4MpahyhVqjx7fIJj02X+7MoaAAfqHn9xQ5M5xzT41vlb/M7f+dh939lLy01dhADi1DQVwEAXsmIJrX54XyLKeyG1/UnKfnfeM55lsLjlc2xaP1MA2kGya8TmXrCf5/MoXd/nL6ySJJKLyy06fpzF4e5Une3c3oW7PabLLuudIHtGH50qceIxYnR3HntW8FlusdULmS67VFz9PXzl+jbnTkw+fEMfYOSjBY+P3LfgJ4tcGZQjx4cTeSEix3uOvVbDxy0Mzh6o8INrW3zycHGElCVKm6I9daCKIUSaIGFyba3Lz56Y4lNH6zx/YZleKJksmRQsQZwmRSw3+hycKIKCqmeN3f9GaowZxQoEnJot8enjE7x+p0mUwJPzZS4utfDjUWPKJ+dLfO/yRmZc9yvnjnBmoZbNknf8mLJrEESStVbA4UnFdNnDj5VWKKTHMkzEE6WTMxr9GNcUlD1bXwegmhYyXMviE4drzFS8zMztpatrfO/yBne2eiBgsmATSokpdNxmNDCcTInloABhCoGVxgyGSUIYJyw3Y546UGW67FJ2TZabPldXO7iWydMHK1zf6CFQpFMfGGgfCgkUDAhjRRDHbHR8Jksudxt9fumJaf73P+/rgoxrZOc0gJFeB1NA0bFo+hHTZZMokbT9iIKjlQIHah5XVztEUvLs8cmMbBVsg1rBJkqSseT7+QurSCm5tKz/nyV0Asc//oPX+eThOqutgI1OwETBomcJVloBCzWBaxlcuNsiiCVzVReEoOxZHJoQrLV9lpsBH5mv8GufPc7vv3yTz85VMFIT0PPXNwnCmJYfsd72eXWxgR/GRIni228s850LK3z18yez70svSmj2Y6qelUWDLm33OH9jiyjR94xtghAGZc/iTsOnF2pvioWaLtYABHFCEMvMQ2RA5v782uZYY8fnL6xydKpEwTb47kVtDDhQy1imgScEW72Iort7Iso7waN08TfaPn9+bZOJos2ZhQqe/ZMb0XnmSJ0/v7bBcsPn+HTxoRGbe8F+ypgfpev75lKTO1t9XFuPVgRRwtXVzohqbNz2Xkt9Xo5PlzO1wnzVfUed5Z0Fj9dvb9PsR5QcKyvCCkafkx825KMF7wy5b8F45MWtHDly7CfyQkSO9wXGJVvsNOwbtzBwLYufPzXFdMUbIWV3G30+frSWdWPLnpURPIDpskciBZMlm+PTZW5v9/CDGD+RtIME1zY5Olnk+FDXbrB/7fYea2KeRmb++FaDJ+bK/PLZeX7ji6f52gtXma0WRo715kaHP3p1mZ89OcWzxydp+zEvXlrnxEw5myX3o4SWr2MwlVIsNXwqniaLgySLAQEfQAxVJxIJUkpubfWIY8mxqSKv397m5kYXP5ZUPRvHFLx0dZXtbsxMxeFgvcBGN2Rxuw9K4scKgcrGMYb35VkG/UgSJQn1mkvFs+lHckQl8PyFVWzLZKMTIIA37rTohzHDdQTJPaLQjyWOrb09rq11ObNgcrBe4AfXNinYBs1+hFRpvChaASEA1zaIYokwBJ0gQiH49NEirX5E2485bJs8daDKtfUuCJguuxhCZJ/JKzc2+cThGq8tNseS74vLTRY3e3i2iSng9nYflGKi7PDmUou2HwOK6bKLl3a7O0FMJ4CtbohSiopr0QliZiseRcdkvurR6Mf89t88y5mF2n339KmZEq9c36Kcmnn6YcxWL2Kh5lEr2LT8mN/7/tt87R98fGw0pi5obSClQigtgZcCDtT0yNJqK+DYdEnfTEJk6g3XMtnuapLopeaVQggmivZYwjgoytUKVV56awM/StJNCiqehSkETT9msuzsmojyuHiUEYHB6z5zapKLS21++PYWz40Z0XknxzK8KH9zqcmZhWr2/0/PV5FK8ePFBmud8KERm3tZ5D+qjPlB23yUrm/LjyFN7QGykaCWf081Nm57rX7IZjfCs83sO7Zz7O1RsbPgYZkGC1WXThhjmbrodmahQpg8uBTxQSZV+WjBO0PuW3A/8uJWjhw59ht5ISLH+w67/bH7wpkZXry0DowuDMb9EfzaC1dp9qPs51MzJV66ukGYJHznzWVcyySWkhlPL5oniw7LkcQTAsMQnF2o0uxHmYQf7i1Mrq93mCxarHcjiOHQhEs3SHj52iYrTZ+/f32TO1s9Joo2Qgik0oWQrh8RSzV2Ybja8ik5BmEiiaWOvTQNnQqx2Q7wbF0AAK1KGF5fCwEF26DkWDR6Ie1Qe0S4lkGzH7O43WeiqCX/6+0Ahe6AC6HHGIqORasfYwhBkGgPCj9WWmafjoAM9tsJE1zTwDYNuqHkuY9UcSyTb56/laktDtYL/NIT01y402SjE2AJTYiHEy8U9zwopIKyq309Vpo+zb6OOn352iaTRZteqAszSaLHUgYIYp3mQaLTPExD8fqdBnZq1BhE+n1bnRDT0KqVASqehUBwcamNaxljyXezHyOE9nO4sx3gWgaJVASxzBQ2y02fbhCn3h06CaMXJBQdC9cS9EJJpxfRDxMUemTmZ45OZPfrzsWuY5kcmSpyoObx8tubRIlOAakXHbpBnBVZfuuPL2bFjGEM0lu+ezFAGXqUxjENFNDo60STpw5UeXWxgWsJHFNw4W6L6bLLSsuHtACmU0gknzhcG0tKhwsotYKNKQT9KEGl6gepFAXbHHuM7xR7JVijr7OZfeLeMT+qD8g4jHtO3dnuU7TNkQLmQr3If75QG1s4etj2dlvk71XGfGm5ye9+5yobnYAgTnhrtc2Fu03+6ZdOjy2Ewe5d31rBotkL8aME19IKJaUUtcK9ZcS47XXDBNs07itgDI+9PSp2Fjyqnk0/FNgWfPGsfmbvPI5x1+aDTKry0YJ3hty34H7kxa0cOXLsN4yHvyRHjp8shv/YDTrYtYLN1dUuv/bZ49QKNstNn1rB3nXR+OWn52im5odSKTp+TJQkTBQdRGqoWPEs+lGCHyUUHZOya9INY/wo4T/e3CKIYn7/5Zt87YWrmTz91z57nChRmKbBoXqBw5MFemFCy48xhaLZj1lv+6y3fS6vtHlrrU0QxQRRwq2tHq51f1zj3UafuarHWivEs0yqnoVhCBQCzzKolxwmija2oY98Z5MvURCnaQumIZirePyNjx7g82fmOVAvcKBeoNnX6RHdMKYbRPQjqeM4E8WtrR7dQF+fMJaIdExAoosEhgDHhImSg2UYFF2LkmtR8UxmKh5+FPPytU2a/Shb0P/e999mrurQjxJaQZypK0ArG4As2tMSgrV2wHZPF0m6QczXX7qJUIow0T4HQayI06KFxaiBJunPlqH9Mra6IT93Us+G//DtLTzb4Mn5CtPle4vyth/zycM1tnsR/TDm9laXq6tt7mxrg8y7jb4ey1Hgp/eIQuligmkgBNzZ7rPVDWn0Y6RU+FHCZidECVioeUyWtElhnMZaHpssUnG1gmSQUjG4p4bv6d/88hP8zt/5GM+dnKIfJWx0Aq6v62MLY0nZNdnsBHz9pRvZdga42+hzZKrEyZmyju5Mpeq9MKEXJhyoe8xUPJ45Use1TaJER6P+0y+d5vNPzoIQdFJF0DNH6ni2NZaUnp4r8aPrm/z7N5bwTIEfJyRSpUkdelToQO3xfRAeZH6UMQAAIABJREFUhLuNPhVvtIY+jmDt5XUDMtrsR1gG/NmVNX79m6/yz/7wjfuu7U6Me049MVfm6mone+4MnkHDBc1H2V6toItij4tvnb/FjQ3tSzMY07qx0eVb528B9z8nH3S8ZxdqPDlf0SlFQYxnmzw5X+Hs0PN33PYMIai42odnvdXnwt0Gb693uLrS4ttv3H2s8zpYL6SKJI1TsyU6QYJtij1f9+HRqxcvrXJpWRsWv5Pr/X7CzmsE+WjBo+JMWkD83b+nFWgfdrK912dvjhw5cuwVuSIix/sOD+rkjOsEfvuNu3zj/CKrLZ+Ka3FkokDRsynaRuZfsNIO+NkTU5Rci2vrXTp+jGUIGr2IbpggpURKqBdsSo7J4maXu9t9zp2YoNm3RjplXzyrF9urzT6v322y2QkxhO52K1SmLpBKWzwuNX2OmAaWYWRJDwMMFoa/9MQ0/83/+1cUbJ0O4YcJiYKCp0nvE/NV/HCbVjA64gCp/D7t1A+6ewO0/AhDKPpRTByPKgpiBSilxxxcgzDS+wyHdqDQKQiWYRBLxVTZYaasCfagw3lpuc1EcbRLEiWSjU6IaQiUGlVxJOloxaDQUXIMpAQ/EcxWHOZrHpdX2qx3tPGlGq3dUHBNpFR0U4WIZaQjGxIsQ2AZBqfna5yeJxul6UeSZmqqOayk2e6G/PD6FlFaLFBK8adX1vn0sQk+dXSKom2y0g4QQiAQTJa0aqEbxHSCGNsUFGxDd30NoRUUpqCUxmrappaNR1LhOhZPHazhWOZIB2m3JIMrq216YUKSSJJUUeJagvmqx3TZzUjq8HsHHemPH66x0uyz1Q3S66L9Tk6mnfqZii5IDCsE/tG5o3z9pRskiWSl5fMXN7axDMFXP3/yvmN78dI6p2fLrLR8troRlqG9MYQwKLkmHz9YY65WeFc6ZQfrBW5udFhpBSOeA8emy/e97mHd/gH5D+OEv7ytR3TqBYsLS62HdsfHPaeOTJXoRdp09VG7qO9GB/u1203KrjmiRlBK8drt0ULYXrq+Wr3T48xCdeR7NEz2x23vc0/O0vFjLiw1ub3tY5o63cY0DP75n1wB2HVUZTfsVBLZpsnx6RJzVXfPo0DDo1f7NTLyfkI+WpBjv5H7ZuTIkWO/kRcicrzv8Ch/7AYmjyXXouQY3NjocGWlzUzFYabiMVly+M0vP8Hvv3yTdj/k5Wub2oBRaGl/kGgX92agTS5dS9ANBQXHJJHwFzcbfPnpUdL35afn+O//7QUur7TxLAOUIpGKdiJRzT6ebYEQOiEi0YkT292QcycmeHO5M0KIb212OVDTIw0Hqloe3w0lpgFVz0RgcGW5TTKb0ItiEnnfJQA0ua/aJhMlN0sSAd0FfXutg2sIgl2s2xQQRgmxupfGMfz/YgXEmgxbhuDyShsDxcmZMjc3Omz3Ij5zatSdfqrk8NZqh/mqx+3tHqBHSMIodb1EYBmCQ3WP9W6IEoqiY5IohRCCsmux1PQxxCAQVEMAvTChXnSIZIgQgumyi1Kw2Q2oFuwRpUTFs1huxruSrXrJwbUMukGEaQgMwyCKFa/eavDzH5lmpeVza7OHH2nzTqUUM1UtM7dNIx1j0QWMJC24tIOEXhhTdCwUgpJj4tgm505MASCVeii5/Ob5W2x3I+oFk/XOvdhSJWGjE2KbBj+6voEhjJFRggH52O4G9MIEwxDYAgq2CcJgqdlnsuzuSkyCKOYHb28iEByse5yaLWc+JoN9DHfuByMI//6NJaZKDudOTmfb2st5Pg5Oz5X4w1fvaFWOq6Nolxp9fvmp0e73l5+e43e/c5XX0rEE1zKZLrv8gy/dG5EYkP9XbrSyER2lFO0gHlvoGcZuz6mzexjDeJTtvZNFvkJlCrABRPr7AR4l0nMvRYtxfj9ff+kG3UAny5hpUXOh7hElim+cX3zkQsTYY/nSo3Wsh0evYH9GRt5PyEcLcuw38uJWjhw59ht5ISLH+w57/WN3abnJ7/yHy3R8PU7R9iPCRHf4t3sRMxWPxc0e3zx/C8cUvHJzG0voDvZ2L8JPO+q9UI9m+HFCO5QUHf0ay9Bz79fWuvy145NcHDLQXNzsYQoBQuBYBk4qf28HCY6lkxtAk2+Uns/vR5KfPzWVdUsdU2AIgW2ZTJYtwgM1Frf7lF2TimeRSB11aZmCK6vdHXTiHgaeC20/5td/QUeEDood81WXVxe3kLsUMAYIpZ7TGl6CD0wxB2MQMwWLTpjo4ovQKR1XVjo8faBCL0j40fpm1qEuOiZSKTzbSEcdAmQiqRdtCo6JEAZnF8ps92IafkwQxXhDyRiWMZjfAM8xsgjRwbGUHBMpLXqR1MWetEjiR5Ijk8XsHAZEbreZ+jBRxDLBMg1EWjyyDEUk4X/9s+sIQ2AgcE0DUyi6YYxsKk7MlFmoeWx2QoqO9rcYGGqGUnFzo8eRSU+PdsSSJ+cr/Oj6ZpbM8fSB6n3HMozXbjcwhKIXSixDEEuFUjohxRX6vObTRI7hzv2AfPzqN/4TtmUw4VpMFB1Krk6KCCI5tls/IIt3Gz5HJ4uINCGk5Fr3KTgG5H297WfqolY/Yq3l0wkSyp7FqZkSjmW+K52yq6tdPnm4zko7oOPHVAs2p2fLXF3t8pUdr5Wpw+uAjA9+HmBA/nVajSakQWrq+jA1wn4vyvdze4NCQj+MWe7HzFV0MTWIJZ0g4dkHxFo+zDvhUYns4J78/uU1TKFwTcFs1aXoWEgpWW35j3x+g+2+E1Jd9SxavWjE82JnUtJPO/JIxBz7iXezuPVBNo7NkSPH7vjg/MXN8YHBXv7YXVpu8i+ev8JGK0AInVYwnKygjQy1oeBrtxs8c7hOHEscV8/FB7HMoh8TqTIzRNMQxIkkMQVRIokSyeWVFo1uSJAkHKwXWah59COJbUK9YNFEO8oPkiZafgSplF5KLc9HwKuLDf7ezxzKun9fe+Eq3SDO4iGrno0QoFJvBNcymKk4bHYCllsBqfjiPugBELAtcV9EqC3AQJCMe+OY7ZBuyzLAMARxojv9EtjoaRI9V3UpuRaubXJ2ocpas8+ri40dHeqYjx6q0vYTXNviI7P63PqhZLLsMF20ubzayYolb6Wkq1508KMkHXVIFRmp94DUQQ84pmCrG1JyTTzbIEwgSiTTZZd2PyJKJN99cwXHMjJFzG44WC8QxoqJok2UKDpBDELgGjr1oexYHJkqZKMWzX5Ey484e6DGj29uoQA/kghD30u2aTGV3mN3GgFHJgpIpVhqBpRdE8cQdPyYpaY/NhZzAD9M2O5FRInCNgWGUISJ/vz1OUuCRPHMkXujHkD2nekGCVNFmyBRrLR8XMug5lm0gmRstz4bUUgkFdfKfEKurXd59vjkCCE/WC9wY73D1bUOrmWkBZOYMFZEcUwQwivXtzgyVeTvfurgfek373RxebfR5+h0acQQcpz6YhAx+rFD9ex3zX40UlQZkH/bFARRAmkB5umD1YeqEc4s1PjCmZlsLGw4jvdxsF+L/OFCws+emOL7F1e5udnDNgUlx+LwZIF/dO7oyOuH97ne9vfdkO7MQo0n5iu0dio+goS56rvjJfIwPHWglo1edfyYsmfdl5SUI0eOUbwbxa0PunFsjhw5dkdeiMjxvsTD/th98/wtFjd7WJYgiCQGmiwn6XiBKTRZnSppc8ogURybKrLeCWkHEWba/QawTYFtGjRjiZEmBvSjhERC0TFwLZPVTkDZNgnjBEPYVAoW252AG34PyxAY6G61HvnQiROa0GvVRNXTpoGDru2l5Sb/7vW7rLcDio7FdEUTcIEgUWQduu1eSMoJNRGHsQMWtiGoFx2klFlE6InpIv/u9SXiRCGE/rIPvAbGIStomJrwBvHoK5NEESO5tdmj5BgoXTVhoxNkHeqNto8fSwwE272Y+arH4cnifd4M3zx/K1N4FB1djFhq+ll3suiYOKZLy9fRna4lCGM5Et1Zci1OzJRp+TG1gsVM2eXaWidNfNBz3obYTUei8eWn5/jWj27RCyVxkmRXwTAMBBLPFmz1wqwQMSi0NPsRvSjhYN3V8aAKCgWTuYqHQvD5M7MsN31+9blj/Ff/56ssNXwUutv6M0cnHuqfUHat1GMj9ekwTIwkQab3l1IKP4p5e73LiZkiF5d7LG71soWciWJx26dW0PddnEjuNHwOD6lFhjFQOVQ9Gz/S40quZdDx4/sI+ZefnuO//NZd2n6MQtGPEkxhMF02WGkFGIY2XY2ihBcvre/74nKvIwx78VwYkP9vnr/Fy9c2mSjafPJIDds0H6pGGHhlnF2o3hfH+zjFg+FiwK8+d+yxr9Hw6EwYJxQ9G4lAoZivedQKzsh+dxKAl69tcma+zMXlOCPoJ6aLukj3iOcxXHj6lXNHMk+IimvSDhK6QcxXP3fyQZt81zDwvDj7AM+LdxN5BzhHDo08jSNHjg8v8kJEjp9KvHa7Qdk1OVArcH29O2IkIIGqq0clXCvh3IlJpiseUZzQCZs0+hFCaJI9UFH4sY5XDBKFJTQZNxCEiWLBs4mlpObZXFvvMlPxODFV5IcpubQtQSR1IaJom0RSJ1ikXpUcntAdddcyuNvoj8xMO5YOrllpBizUPMquwWY3phskFGxBN0gIk4Qjky53tnZzedDjALNlh+WmTyxVZr4XJQrHEkSJykj8MMz0eg22O1u22erFRHJ0TwIwDK1OUEAnSJiterT8mI1OSNE1OeWVaPUjKp6tVQs97ZwfxQmXV3p6JlspfuuPL/LWapt+GJFIgWkICrbJdMkmklAr2iAEC1WXKJH86ZX1rAhhG4KJkkvJMQgS+Ce/fG8u/GsvXMWxzBGCurMDvhOaiB7jf/nT6wSJlo0bhv5Mqp6t1SBD5p3tIOHwZJFf++xxfvNfv8GdRh8jHfeZrxYwDYFrm7T9GNcU/Ivnr7DWDqgWdAJHmCgurbSpFW06QbwrGZmve7T9iFY/ohMmmAaZh4ZUOtJzIlWPvHJ9m6Jj8jPHitm5T5ZdWkE3/Q4YxFIbmk4UrbEKhQG5PzVb4se3GvpklVZjjCPk/ShJVSqCRIJhSLqhwjINPjJbJogS3lhq8UvpSADs3+JyryMMey1YnFmo8T/8nY+NfBazFfuhaoT9WjzvdzdwuABzbb1LrWAzW3FpBzG/+MTsyHdi3Dl4lsErN7Y5OlWk7JoEUcJ/vLH9wHGOvZzHQAk2rCD56udOjvWH2AtJf6dE/r30UMg7wI+PvIDzwUMeNZsjx4cXeSEix/sGw+kXA5nzbiZmAoECpsoum52AdhBnbFrPegtsU3B8usQ/TGXIv/udJnEiMYQeNzCFJuGdUGpVhKETDmzTJJaSesmi4tpMlHQHsR/qDiFAJHVXPkokkdSqCsswCBKZmZ+ptLu+1Q2xTINjU0UO1gvZ4r/gGLR97XxgClhr+ygM6gWTgmvSDRJKrsnJSoEggWohobGLkZoJXFntgFKUXIsXL63qhA4FCboC4RoCCVlyh4EuLgh0QcY1YapSoOzF3NzsoVL1hClSrwilFRUGIAWU0pn6uarLpeU2btpF92wd1Tdddjk6VSKME8quTcEyubraoeWHbPciBCCEIo61qWfF85itupRdm//sEwd48dI6syWXE9Mlrqy0MYQ+3q1uwFpbUXQsfuuPL/Lbf/MsZxZqD13M7LaA/fVf+AiHJor8zn+4TLsfUfYsPnFIL2xfemsDzzKRUt7XwZ2uuPTSIsFqK2Bxs8dk2eFjk0XtxxDGXFxqESWSVl8SS0UidXFkpdnnU0cmMjJimzo68o9eu8tzp6aYLbsUD5ustAJWW32iRGEIoRUjpkHRuffoFkAniEZi1RzLZK7istoOWO+EuJbBydkii1s+p2aj+8jPgNzXCloRcHGpzXY/5rlTU/yjc0dHFvrPX1jN5PSebXJnu8dWNyROJFOercc6hC4wLTf9kTSL/Vhc7pVAPqrnwqNKjvdr8bzf3cDhAszA+2Lge7HzGMedg2NqT5JhDNRS7/Q8vvKxgw81ptwLSd8vIv+gz/zdJLx5B/jxkBdwPpjI0zhy5PjwIi9E5HhfYDj9Yrbs0OpHY6PdMhO2KGa7GzFbcTkwUWBp2ydMEuzUdDCRip85MsFXv/CRbIEyV3W5s23RDRK6YUzVM4mlIpIK2zQwLINDEwVMw2C9EzBTdnEtg5Yf8cyROuff3qLiWch0HKHoWEyVnKxQ8cYd3UmueBYF2+BuwyeRkl6oeGKujGEYnJ4r8T//6dugdEHAMrQhYZhoH4SKazFfL2IYgjD26QYx14KYqmfx1z86z7/58R3CNF3OQHsmKHSBIE4kQmiDzSjRxMNzDNq+foNra1m/ZeioS4n2sDD0hAUTJTclLTqVYqrksNTsk0gFqW/FYDREKeiGCR8/WAYUP3x7C6mg4pmUXO2M//RBLXl+8dImzx6f4uJyC9c22N4MYWg7oJM5Frd9njpQo1awubrazchmoxeSKK1SEUAYD0o8is1OkC1ED9YLvH57m6trHVr9CNCmlkeny3z7jbsPHBP4yscOcmKmnC1yB8T17EKVXpiw1glHOrhfe+EqR6dKLNQ8rq13iaWiG+jRjuMzZU7Plfgfn79KmEg8y6Dpj0YC+rHi1dsNnrUMio7Ba4s6OnKiYPHmUovJkoMhBGcWqhyfLnJpuc12TxdJTs+W2e7fk82fWajwxp0WbV8nPWx0fNbbPtvdENc2OT5dxDQMbm32mKuOVyj8xhdPD5F73TnfjXjpGN0Kf5lGQE4UbdZaPiCoF/RoRxBLDtY9NrvhyHsfZXH5oMLkXooGwwWLi8tNmn39PRr4abxT4vI4i+dx5Ha/u4HDBZiyqwtF270IQwhub/c4PVvm44cnsnPYGYXa9COOTRXwbDP73dkDFYId0cM78U7PY3BtXri4im0KnjpQxRA2q6kHzfcvr/HEfIVfOXeEq6vdd5XIv9uEN+8APx7yAs4HE3kaR44cD8cHVQ2WFyJyvC/wjfOLlFxraIFhZL8fkI/hxeGzxyd5+a1N1toB9aJFtWCx0ZGUXYsD9QILNS+T2A9wfaPLejsAoRcwidQpCJYpmC47+LGk7ceEiaTVjwkiSdWzmKm49+XUF22T0ElYafs0+yEzFZdEaqI+SCkAWGr4RFKx3Ar4/JPTvHhpHcc00nQGwVorpuiY1DwDqbSRYSy16aUzMMxM3dwNIagWHDp+RCwVnmUgFfTT9A/HMlAIYilJpE7qqHo2niWJpMJPZzNmyw6NXkR/4AGhtKKjGyYItMrkUN1jtR0yWXRYSV3tTQNIdAGj7BgkieI/3tgkSHTkpkLS6EmCWPHs8Qmmy16a1JBwcanJldU2Qql7+x2DS8utbGxhQDa/e3GFfiQz7wfTANMQBLFiunwvWrXoCP7i5hamEERxgkL7hxyqx/ze997mifnyfQvYb56/xUzFyx7sXzgzw9XVbvbzb/+tp3Yl4ws1j2urfe5s97R6xdEeEb/xxdN87YWrTBRtgiihEycj3h4Css/uymqbfiRHoiM7QZIpScI44YdvbzFRtPnMqUkuLrW5ttbl9JxWGXT8mEvLbY5NalK83Q24vNImjHVRyjIESw0/vT/lvTSSFMPkZ6+KgAEBf+ZIPU3NUDo2VSokWiXx9MEq7X7M1bXRuNq9Li73Wph8GAbns7jV42Bde5XsF6l81MXzbuS2aBtZEWmAd9INHC7A9KOE1VaAIfR3ZrMTcL4TcO6kHrMYF4Xa7MWcnC7x8SP3RjGa/YjZir3bLoF31tUcvjZSSVAGry42mCra/NVSC8cUmEJl98HBusenj0+NbGM/ify7TXjzDvDjIS/gfDCRR83myPFgfJDVYHkhIsf7Aqstn9myQzeI2e6FBLHEMQWN3r2O6vDisFaw+ezpad5catH2I/xEMlF0ODBR4NRMiZmKNzIL/e037vL67QaJVDiWQRIrgpTwJ1JRsE0SqZ33C7ZJrWDhmAab3ZC5mqe//MfrfO/yBne2erT8iCSRmIagHcs0ttPANgTr7YA72z36kSZ+p+dKnF2o8n+/chvX1tGezX6MkpKio1UZKoYjkwXafsRKy6fs2limgZQgUpn7tbUuC1WPq/2IWOqRkgEsAdNll5WWLpL4JPixohsmHKp7bHcjGv0YAWx0QyyhR1FiqVUVJcdkpuJxYKLIF87MpOoBXYQI4oTWIJ7SAMfQBpNhnNDsxxgCnpivEEvFctOn4ppsdEJmqxG3Nrv4keT6epdeEBM9JEa0lRLrX3xiNvudQBeKNruRLvYICGOJQtENYoI45m4jZnGrR71gs94OkUorPRxTsNwOKdrWfWMCfhTzw7e3+NyTs9mD/cVL63t6sB+sF/jLxS1eu93EMQ2KtvYkubTS5ttv3M1UA81eyHZPjXh7OJag7Fl0fK1qaPkRlbRwFcSSsmdR8SyWm5qYfO7J2YywGAcFf3Z5nfM3tjg6UcAyNIEJyoqvpAkOiQTbMjhc0qkZvTChE8QcmSzix6MfwOOQn+ExjoFJ463NLoYQI8akpin56udOjhR29rq43Ethcq94t0jloy6en7+wSpJILi63MjXLfMVF2QbNVMGzX93AwTH8X6/cykaqYqnVUKYBf/z6Cr/+Cx8ZG4U6V3FYaYcce8QC0jvpao4+253UtFbw+t0mnmVq7xVTZJ/d4nafJxf2r3izE+824c07wI+HvIDzwUUeNZsjx+74IKvB8kJEjvcF5qoea80+7SDBMjSB7IWa6A9iDncuDmcqHmcW9FhAwTaZTDvQry42eOZInamym5lD/t7338YQAiW0kWMsh6IwFdxt+phCFyTitFgxVy3w8cN1jk1rqf2gQxvGCUGkzS118cFAKTg2WeD6ZpfNbqAJPqBMODJRJIwT1joBAAXbRClt+OjZgoJj8aWn5pgue3z3zRUsQ+BaOqLRMgWOaWIIQcuPmCrZhGMk0rGCzW5IyTaxLQPHMikr7Suw1PB1EodtYAC9SBINUWOZkpQwSagVbL79xjK3t/osbvexTcGnj07wX3/hI/zjP3idTj9ksxex2YuQqXGlbQo9DtCJ0vjSmKVGn06gO/pxnBArHlqE0F4HMSstP3Ouv7TcxBBaWaIVHwo/0sfupEWfl65s8ItPzHBnq0ejG2bz7VIppFSIfsSheuG+MYFLy20mio/3YB+kbRhC6MKW1NGvEwWbb5xf5NyJKZr9iJ87Nc0LF1fZ6IQodCxqrWAj0O+zTAPHNHRiShod+dSBe9GRO+/56bLHRMmmtRlxt6mVKgtVj7JrcnW1y5HJIs8en+SVG1sEUcJMqrJoBzEnZ8qPrVAYxjgCPohIHUfKv/JIW9ef+ZWVNqZQtP2IyZJD0dEd+9VUnfMoeDdJ5aMsnt9canJnq49rG5kJ5NXVDocmC/yTXz69r93AQfdkO73nDQFKkKawCG5udIDdo1AvLbeoFez7jmenNPT0XGmk0LRTUbTX8xj+jAaGqY6pvw8FW5utzlZdYDS5BvaHyO88L8cU+6pS2Ym8A/x4yAs4OXLk+DDig6wGywsROd4X+PyT0/xP33kLhcK1TCxTIBV84mAtI4bjuiEDMimlYnGrn0Vo/tXdJnEcc229z7/58R0UULQFRcfSMXSD6E5DUCvaCAHNfsJUycaxDP7WJ/TCRiqtkjh/fTPr0N7a7GJberxCKnhyrkI/jHlrvUOc3GPbCggTOH99k+myHt1QCtyCVl8IAwzD4ORMmemyfsA4lo6lnKm4eLZJN4h1MgPaE+LVxcaIxH+4JOEHCUfmyyy1AlCKQxMephBs90MO1guEiR49UTsqAgqdBhElAd99c5n1TkjNM0kS7W/xvctrLNRdmt1QF1O0ZUS27yBWrLajkW2GEm5utNNzspgq2TR7EfeXUO5BoOXjs2U3Iz1ff+kGJ6ZLdAKtHohilcWiVjwzG6/Z6ob0wzgb+xiY6yVAGCfMpwkfwyR8uxfxmVOjSQB7fbCfWajhpvdAmOjRitmqi2cZrKaFlIFq4PNnZvn+pTVdKHIsUIp+JCm4Jv/FZ46yuNnnz9PoyE8cruFY96Ijn7+wet8M/3LTJ0oklmlQsE3CRHJ5pU0vSji7UNPpFzMlXl3UniVKKRzTwDSNx1YojDv/ce97p0Rq8Jl7lkEiJXGiWGr4HKh7RInKTDIfBe9mF/VRZjZbfgyCzMzWs7WJZMuP970bOOie6OxfhWHouGIpBQiQqfXkwXqBG+udTBExUGk8daDGb3zx9H3nOiwNvbHe4Q9fvcMzR+ocmSo9UFH0sOs0/BlNlz0+dbTOhbstLEOglOBA3csMWoeTa/aDyI+TvK62AqRSHJ0qvWuEN+8APzryAk6OHDk+jPggq8HyQkSO9xyXlptcXukyWXZo+xFhIkmU4FNH63z0UD0jhuO6Idu9iKcWylxa6RDGEscSSKV4824LCdiGTn2IFfQiRSIjDGFQsAV+LBGGYK7qUXRMrm90qRYcagWb9bbPtfUuW52QybLD7a0eEwWLO9s9wlimM9dk6gQBdEOJa4rUDPIe5W70Yz2mIUCmRppaamzQDRPmqy5SKW5tdGn29VjBrc0eM2WbomPhGIJeGHN9IyZIpfWmuJdgodJ/YuDmZg/HMnEsHf1ZLdg4psF0Gu3Z9senbgCEiaTRj1BSsdmNsU0whSCMFf/HDxf1dZR633tBL1Uu9KIIP9p9vwNYpmC24jJf12RzWIpW9iyurXdpLW5jCEHZ08kojmUwUbK5udUjSotARnptBpCSsST8509NYVvmyDE8yoP90GSR1o4/DM1+xFzVG1kwd4KYL5ydY3Gzy+XVDkEima26fGS2zFtrPQ7WC/y3Xz6dHVutcC868npK9gYz/Gstn61uRMExsnGijU5ItWDR7McjBZBPHK7x41vbrLYCpssORdvgxEyZr3zsYEYMf//lm3syPXpYJ3y/TJMGn/kzR+r84O1NHBSGUCybiQbgAAAgAElEQVQ3fAqOmSWW7LUAcGm5yUbbzwo9ZxYqeLa1L6TyUWc2awWLZi9Mxw4MgliilKJW2P8/w4PuScE26AYJUt1TCRnAZFHfs6fnSvyrv7iVKbhWW3Bro8OXUkXSMHZKQ1faASXXYqUVcGy6vKuiaC/Xaeez3TZNTsyU+RsfneNf/uAWyw0fpbQXjWkKvvq5k/tG5MdKXieLrLX6XFxujZil5oT3vUdewMmRI8eHDR9kNVheiMjxnmMwO20YAscyqRVNyo5FlIwSw3HdkJ8/NcWFJS0jLrsWW72QMEyQpKaAtkkQSUx0ZGeYQMkVBFGCZQomS7Y2PowSJovao2K+4vDqrQakxYaFqsvl5RatfkTZNVPJsCKSIJC8tritIwvRDUiptAQ/kfc680opYuDwRIFelBDGEs8xqXg2x6bLWra93efjh+oUXZNXbzb0Qt9JKDom9aJDwTa4tt6hG0oGdY5hbYMBRInkYL1A0THpBAmTJQcpS7SDhEY/wjbJUjd2QirwI4lI/RWkBCkUxiBlI93ngOTvVGQ8CP4DDCoHqBYsPpGOwsCoFG2mov/9V3eaxEmCYzlMpqag/TDWBRLToOxAkCiU1JINA/37LB1jaH8DggTjH+wPI7u/cu5IZqBYcc374j2HF8yDbc3Xi7imYKnpM1crZOaJw53k4SLB4laPE9NF+rFio+2zno73BJEksDWhTSS0+jEnZ6yR78id7R6mYfBLT8xwZKpE24/5+ks3hjxA9kag99IJ3y/TpMFnXitUAXj9bpNuEGObgv/urz+RFVH2UgAYft3A6POHb2/x3KmpfTnWR53ZPLtQo2ibI+qWY1PFEd+S/cKge3J0qsSNjS5BnJBI/TybLLn8wukZAH5wbRMQ9KOIOFXZVFybH1zbvM+LY/DZDIq0V1baFG2DIL73QBmnKNrLdTqzUOMLqcfJMPE/MVPme5fWubXZw48lnmlwdLLIiZn9u2bjJK9BHHNhqc3nnpzNfFBevLTOiZlyToJz5MiRI8dPFB9kNVheiMjxnmCY5P341hZKKkqOLhqEkWQjDOhFMQfqBT59rM7XXrg6lhBeWm7y6998lYmCRdExMQ2XVj9iOx0D8GOJIbSc31CauNeLNq2+4JnDNRYmClxcarPdj3nu1BSfOTXFN84vEknJdNnl1GxJz+XfbrDU6BPbJmXPwu/oUQSFVggYBoToaEkhdMKFaajMDLLimfQjScm1mCw5BLGk48c8e2IyS1k4NFHMFulf/qgmEheXW5xdqPIXN7ZwbZP5qseNzR5yDK/XKgHoRgmWZVD2LA7UPP7upw7yz//kCv0gQTxEzSBVOiKBLjhYgrH7GsDcoT54HJhoc8XPnJzGMIzMH2JYirbe9vnh25skiSSSsNL02ewEzFc9TMPg3IlJDAGLmz0mitrcLpGKfphwZKq46xjBbg/2vZDdAVEbJk+DeM8BLi03+db5W1lHfr7q8ue3m/TChPV2wMcO1bIiyyBWcni/ry1uYxsGx6e1+sI2DUqOwo8kvTBBKu05Ygh46sA9UndmoXbfPTX49zfOL3J2obpnAr3XTvi3zt9ieiiB5HFUEsOf+en5Kqfnq9nPg+u61wLA6OtsZp+4t+33IoJRdzR6nFmojhS+vjykPtiveK5B9+TkTIm2H9ENEsJEMld1OTRR5DOnpvjaC1f549eXiNLRIkOYxImi0Q/5/66u37fNwRjH1bUOrmVQckx6YUKYKDY6PtNlb6yiaPg6bXR8rq1p5ZchjOz8Li03efHSOmcXqiPEv3Btk7MHapw7OZ1tb9iEeD8wTvJ6cenx/WPeTXxQ49ty5MiRI8eD8UFVgxkPf0mOHPuLAclr9iMWah7dIGarF+FaJgs1D9f+/9l7s2DLrvu877fWns587jz03I1GA92AAII0B5A0LBEkhTIdl+OylFQilR/koiMneqCjKFXOg6lUxeUojpkqlysRq2RbJT7YkkwnilRGBFmkKBLgBICAgG70RaPnvvNw5rPHtfKw9tn33Nvn3r7daDRAan9VXXVvn333WmcP5+z/t77/90mS1H9hsHo72HZQEF5YagLmxvz06UkQgk4qQdZak9XbqWM8els9sNIMiJOEhbUuV9Z7/PQjM/zmL36Yf/K3n+ALTxzm2ESJv/5T83zi1GTm3SBTA8leGNPyE6QwbR+eLZioeJyarlD2JApTuEfKkBBgzBxnqx5ouLjc5rWbTdPqUXb4xaePA+ZhvVrYyQtWCzYrLZ9qwaZSsAlixVS1QL2ws50ADCEQRIoo0fSCGFsIgjjh1RtNTk1X+IVPHEXInQqK3SjYxkRPs00uKL030TDY7oCdGrdBpP+0gIdnK5yYqtwm176x2eObF1f5o9eXuLXVI1YaR5q/C2LNjc0+Ld+QTv/Zk3MUPdOuEESJSUPxLH75p0+NHH+/h/rhIlYKkf08IAsG+MITh/ndv/80f/Y//Ay/+/efvo2E+Oq3rvDGYouxok2jF/KNi2tsdAISlbDY6PPK9QZrbT8rYHePO1XxQBhlgGfLlGyTOLak7NoUHUmt6FApODsKWrj9mlpr+5xfbPLmYpPzi03W2tvGj/sV0Lv30/Fjwijm9ZsN/s2LV/j9l2/w9nKTP7+0sed9elA89/hsZkSotM5+Hn5ve90ru+d/0O3uFYfHire1Ou3X2jMgvupF4/NRLzo7rvfdn4v3egyHxzoxVeHMXJVTMxU+dnKCj56YoOhI/tfnF/jmxVXCOCFJTGRsnGhsS6A1rHfC28Z97vFZFlaMyaVnS8qehdKaWtHm7RVjgnpto8t62+dXf+81vvLCAheWmtlxWu/4vHytYVpTLIljiez97XW/vXqj8Z6ew8H72n3NbfUizs5X39Nx7xb38/rIkSNHjhw5PgjIFRE5Hjh2r2hWPJswDllrBxyfLGFJj4pnc2yyxMJK946rn7/49PFsFfn8YpMo0dQ8STNQxiCSnS0EIvU6QGlubfU584nynsZpYFbxtroRBcfm1HSZIDYRfBXXwrYkGlhq+lhCGDVB6kmxDc1bK10qrkXBtQhjRRgrFhs+//yFBc7N1/H2cGmfrZlVxoH54FY3pNFPsraIQTGvtGlJcDGmeAurHeZqLmMlj69+6wpFR/Lp01O8cr1BFMf0R1g2CBRayXTGB0fWfnKH7QRQdCS9IbNM1xZMll1+4+88MZLpHfS2+7FpR7EtQwj1o8SYZgKehDcXW3z70gYPz5TZ6sUsN/v4cUJRWPz2S9cBRpIEeyke7odD8eA6DxNFGCXcavRR6VuPlYlWVUpzaa2La1sjUzJOz5T54dUt2v2Yw3XjZdL2Y2aqHlGiaPRjgljz8Ew5I0kGx3H4On57pcX3rmwZHw2tWesEtPyYDx8bY7o6eiV7gN33QxjHXN/ycSxBKW19+v7VBofGCrfdp3erkjiI/PCgpk3vtbnTvfRs7reicb/juXaPNbjmbzV8xlJfCq0Fidapr4rG0gIhzH25e9yz83WOThZp9iLaQcxE2ficrHdCVtoBp+MEKQSObTFRsbN7akAmX17r4KYGM0Gi+fAxY8w6ONej7jfBe5teMXhfu6+5T5+exH0X/jHvBX6S49ty5MiRI8dfTuRERI4Hjt0PnSXXrBY3+hH+ShvXkkgB7SDmjVstjo4XOT1TySTsuwvC4QfJlXbAbNXjw08e5vxik/OLbcK0t0BiIixtKYiVphXEHBkv8dsvXd9RpJZcwde+u0g/SlKvBBODWXKNKsGzTQxmox9jSdKVahs/VlgSHMsYRCidqiNisgf9k1MVukHMtY0um72AV65tsdoK8GxJtWBM0oaLmr/79DH+9XeucmOzTyeI6IcqTQCRWFLQDpIdBECYGMVAnChubPk4liRJFK8ut/js2VlKrsUL51cZpg1cy7Qx+DE4lqbiWTwyU+bSmunLTuI75G4CrrWdErIXNGQkhPHvkHiWpOI5I3v8n39jheOTZZ5IDUvbfoQA+lGSET5aw2onolJ0GSvarLVC2kFE049wpCSMEy6vdvif//ACwIHl/QcpYu8kkx5c51GsuLrRy5QlMiXCbKlo+ybydDglY3jcqUqBR+eqtFPV0FQlLf66EYtbfYqOxadPT470ajgzW+Zf/Ok7dIOItXaIYxkPlvGSw0YnZLLicmm1syOlYxR2F9yb3QjQFB3bmAdKc3Z7u05+EMd859Imn3l05kBeFAPcSX54UALgvTZ3ut89m+91PNcwMVb1zLmrFmwaPXNfJYlGWyZh5dh4aeS40xWP1VaAQKCBsZLLTK3I0+n16trWbffUwkqXLz5zkn/4u6+BhmrR5rFDNaarhSyVaK/77amj9fsa07kX9iJt3utx7wY/yfFtOXLkyJHjLydyIiLHA8fwQ+d6x2ezG9IJErTW+JGR1VtSMFMr0OxFrLXvvHo7/CA52Pdfe6TAX3tkln/17StUPIvNXoglzApglCiCGJq9kFZ/O3ryj16/xb/69lWCOCaIFDpt6ZitmvQJk1IQYEtBkJj0i4GcN1GaQ2MFllvmdUsKBIJeZHr5BwkbKy2fMBkU5GaFcL0T4lriNpd2gMtrHfqRUXcMKAHbEqg9zBsiZcwyhTYP8wsrHTa7Pv/uB9cJYoXSmrGihdKCThCb95Duq+BYfOqhSc7M1fipoxE/uLrJO6ttwkSP9IqQmOMjpYmyPKiWQmOiUxFG4TDc9jAo7N5cbPLkkTr1opMqQyLidB4DrwuRHotOEONYDkstHz9KjPJAmvYN29L0A8X/+c3LGRFxp4f6QRG72QlYTpMqbCn4lWe3Uxvu5CFxeKzI1fUO650g897QGPLEEmBJSSeIOTpZ3vF3uwsgKSX/019/NDOYrBZsZvyYVi/i9EyZ5VbAwmqHWsF4UAyO4++/fAulNOudNIlGwWytwGTFw7Mt2kHESjvg6aGUjlHYXXAnWnNsvIif6DSpxpw/f1cs7HvVZ39QAuBBmDvdz57N+63g2E2UnV9q8uhcjVrBwY8SCo7FobEC7SA2SgYhqHqm3ef0TOW2cS8sNVlpBbT9mIpnEYQx37u8ybHJEr/23CP81rev7nlPnZ2v8/lzc3u+v71Ioy8+cxLgwOfwbj0URm0/GK8TRNxq9KkVbB47VH/fjcF+kuPbcuTIkSPHX07kRESOB47hh87Xrjdo+zGeLYkTRZL6K1Rsi5lqAdeSrHdCph3rnlZv236Ma0sKroXsQS9K0IOVaaAfKUDzj77+OmGi+ebFFfopAeHZEiHMNs1+xFy9lCoHFLYtsROFFAKlwbGg5FqMl1yWmgE6NaxUGmOcmGjQmsWtHmudMJuvZ5mCoBfE/MVii//8qSM7zNpeu7FFrKBWsLGkYK0dEClo+bdLD+w03QLAsy1EaiTZ8kO2ehG2JYlic4wb/QTXEpRdi36UEKdJH596aJLxsstLlzdo9yMWm30myi4dP6YbJpknhMas7NtSULAlkdJEib6tRWPwuyshVOBKkSlU+rHi2IRHrDXVgs35pSbXN3tZcf/2SpsfXNni46cETx6ps9kJWGkFCJGmk6TRKEprNrsh/SCm6Fh0/BiRzk1pCGNF0ZHc2Opl87rTQ/3Axf9f/Ok7RIlisuwyXy9kzvl7KSqGWxFcS/D6zSZKQ8E2SSuDNhajTpDM14v8+t88tyM9YK/ieTDu4P/HysZroOBYVD0bP0p4a7lNL0pYa/tc3+hRKdiUXIkQmiQ1IpyseIyVHBKt+RtPHOJLnzuzz91KNq/BHH/+N3u0+hHTte1jt9ryibph6jtgyInlls/PPDK9Yz/3awX3oATAj5O50/1UcIwiym5s9Ck5Fqdnyrx8rQGYeN5D9QJbvYha0eZQvch8vbDDNHaA599Y4ehEibm6x6XVLi0/ygxxD6Ii2u/93Yk0Osg5vNs41VHb/8bzF5FCcHSixKNztR2Gou/3dfTc47P8xvMX2eyG2T02UXb5teceeV/nlSNHjhw5ctwrciIixwPH8EPnjYZP0ZXMVAsst3xcS7DZjQjTSLh60SFKFLWCnfUhlxzJb3376sgVr1EPtF985gT/6ttXQYjMcwAgARr9CFvCn7+9xt966gitXgzC5NU7lsyKxjA1gVxtB2hM0V4vOiQKjoybuMz1TkAnMAV+rDRhokwLgiXpKrNKf7PhDx8K2kHC4laPIFEIBFGS8I23Nllu+SRK0+xF1Is2ttG/U/FstkYZPLBNQgAEUUKt6GAJwWY3REpBFCsQRilh2ig0iUpw0h6HiZILwCvXG3i2MZPzbOPpMFZycZ2YZi9GaxOFKoUha45NlgF4e7WDUibVYoDB0VYairbAc22c2JBBSsNmJ+Lw+KAoiTk8tp3y8PjhGi+9s8mbiy3OHapSKdiZuiB7r9pIyqVt2lSOlh3WO9vjGz8QM1/H2vbmPUjR9+KljYyMEkJQKdg41t497btbEdp+TC8yEZtmMgonJUeCOKEkJb/ymYdGruTvlfIx/P8//5svIYSg4Jhe9oJjEcSKZj/m5lafimdRcMw/raGrYjq+UR61/BjHur3YPAhGxZb2ooQj46bg1OlZLzoWvWAnYZav4O6N+6ngGEWUzdVcvn1pg7GSQ9GW9MMEP1Z8+vQUnzo9ycJKd18lweCal8LJTHyV1iw1zWfane6pe31/B1U53K2HwqjtN7uGJH78cP1A+3jQkKkUbHCPDX7PkSNHjhw5fhyRExE53hcMiqo/Pr+MKwUF185UEdbQyn4Qm2L+2kaPbhjz/725wuOHqjxxdJwrax2+9G+XOTpZ5Nx8PXtAHVXIvXajyQ+vbRHG4Y4UCIlRLCw3Ay6ttLFs46mgjYDBFKGAa0mafoQQgrJnVqAdS3Kz0We17TNXK1AtOHi2pOWHNHoRSg1aB+DR2TIXl7s7TCZl+vNSy6foWMxUXL7z9joNP8a1RJZy0Qli7LS/37EPFnSTaOiHMYdnqmz1AuLEvBcLgWVpBvXhQLFRcIwy4hsX16gVbcaKLlIK/srxcV672aTlxxyfKDJejFnrRNiWwJFgSYswTmj0IrTWRHvYScQaSpYADZ4l6UbG2C6ME5Yaff70rVUsCf5UiShJshXXkiPZ6AR851LEeMnh7FyVN5fbyFTBESmz74KAYsGm2U8ouhb9ICFWg1YRQRArnjm9HQE4KIp+56Vr/MmFDQSCp47u7BH/80sbjBXtTG3w8rUGTx2rc6sR41mCby2sESaKWsHh9Ex5ZCvCXK1AL4yxpKQfxqx3AsLYKGkena3wjYvrLKx072rFdVCYXd/oEiUmJaZedAhiBel5v7rRI44TPMd4Qggh8CxJpNjRZnIvxdWo2NJTkyUcW7LcCggTRSEdd2Glw0TFe9/67H/c4g7vl4JjN1G23vFZagUUHYvJsptdA//jc2ey8/mFO+zzICqiOxENe72/vdQMA6PLg6gc7tZDYdT2YayyIv8g+3iQGChSBiQJ3P8o0xw5cuTIkeNBIicicryveOroGN+7vAlCMFZ0uNX00cqs7jb7EY1eQKsfY0tJwZbESvPqjSZxotnoRSCg2Yu4ut7hl3/nJkIKio7NU0fr/MLTx7MHtJLn8F989Bjfv7LJG7camd+BHynilJn44bUtDtcLXNvoAmbVOmt1sCBWiucen8OSgj99a5VWPyaIjFlkouDsfIWbm30sKZEICo4x3RRCcHXTBymYKFpIKWn0QrQWRl2gYLLsYlkWnX6EZwlsSxInGs8yXhRbvQjHSkyxeUB4jsWp6TIXV9ooNDZmJS1KSQiBccc/OVXh1lYfRxqVRBgrNrohHz85zsOzNeolh28trHOr6RMlxh/gv/npU5yarvAv/uRtvv3OhvH0qHq3KT6G0Q0Vj8yV2OhEWKnpZ5j6Xnzq9ATnF9t8++0NozYpuVQ9m5Yf048Un3ponJPTFX7v5RtUXJtumBApTeoLSjdMj4sQfOTYGAsrHRr9iCiBesHioZkKv/LZh2+bUz9SfPzkZFYoD4qc599YYbzkpLvcVh2cX2zz+KEai02fTtor3w9jXnpnk14U8+ThOi9d3jCvFWxKjuTKRkDRFjT7CbYlKHk2rhQstwKOTpYObOIIOwu2qYrLlbUub6+G2NLcP1MVh2sbAa4UKCmIEs1Sw0/TWhJKrs3HTo7zi0P3xr3g1HSFp09NZgXnn761wmKjb5QvjkWcti2Nl13qReeOK+DDhIFniUyx827Ig7uV6v84Yi+iZTdpcGm1ixCCQ+NFnj41CZgidmGle0cCYoCDqIjuRKTsNd+91Ay//dJ1zs3XDqRyuFsPhVHbuyOI3g+Kiic3q8yRI0eOHD9psL785S+/33O4Z3z1q1/98he/+MX3exo53gUOjRV4Y7FlfAqUpuhIiq7FyckyrmOx0vLxbIvD40U6YZIVhLeafaarHmXXYq0TsNz02exFRLHCjxLeuNXiGxfXmK66nJmt8eZii5YfM1ayef1WCzRmNRlSPwHjWfDR4+M0+hG9MMkiOAcJD7HS1As2idIsrHTSNg+NY0lsCR0/plxwsraSMDExnVLC4bQQCmJF0bWy95EoTdG1+BtPzvFnC+u0AuPFEEQJQsB4yfgzgEnvSDR3hMC0TUSJxpaCQ2MFVpoBCUblMdiFLQxZoRikekCt6DBWcogSzc1G38wljIkS+MzZWU5OldjohvzJhTWavZCCa/H44TE+fnKS5ZbPajvYe14CqgWHQ2NFPnV6EiP5MIafjZ7xdVhu+iQapqseQZzGr2rTKnN0osQPrm5Rci2iRBErnb1XhPHFqBRsnjwyRsmzGSu5PDpf5dmzczx7dprvXdni379ykzcXW4yV7MzUsV50dpANN7f6piC2BW8tG7PJXhgjBXSChGMTJSYrHvP1Aq0gwY/MOdVas9WNsKSg5Fps9ULeXu1kXiQa08oyVXaZrBZwbUk7SDg9U8nGffohUyReWGryte9e3zHf9U7AP/6D81xb77HW8Vlq9OmmRJhS5npb64aMF23m6kV6oSJJW2USpTkyXuJjJyZINHzk+HiWQnO3GE4UmKy4XF3v8r0rmwig7FkkStMNFZYwBOA///kP8fnH5nj6ocmRYw7vT6P47uUtbm31OTpRJFHw4jsbHJ8s3vV8v/ZdE9s66vwOjvOPM3afh5YfZ8fq9EyFF9/ZAIxHzPeubNLqh5Rcm1rRpuTauLZksenz+cfmDjTedLXA8ckiN7f6LDZ9pqvegVtHLiw1+T9eWOBffvNyqqLxdpzbP1tYZ7LiIoZaDVxb8sr1LZ44Ur/t/0fNe6xkZ+/ZtSWtIaJk1LUzavswVpQ9G8+xDrSPB4nBd9jgOgYT1Txd9X4iruccPx4Y9d30ft8bOXLk+GDj13/915e+/OUvf3XUa7kiIseeeBCy5rPzdX7tuUf2HOev/W/fYKbiIqVMWzc0jiVo+mZFPYiVKVhTM8itnikYtYbrGz1+7fdf58VLG3zy9GQm8R0rOjsMIxMNOoGiK1hs+hydKNHoR0htHlBdKRBS4tmCv1hsZ5GdAP3IbNOPFJ0g5vB4ifVOQNmziZXCkoKiazNe9tjqhmz1Y9Y7IY4EhMCyJM8+OsUfvb5sCm62/RvsOKGXRuxVCiY6tB8mdPfJyBzEWpqWC7i01mG85ODaAj/eKTqONfSChG5gSI+CLTk3V+XVG41U5aF5Z7VDP4z56IlxwjjhRzeaeLZkrGjzxmKLrV7EJx+aIEoSrm/2shSNUZgsuzxxZIx60WG52ePVa5vECjzbGF5WCg4aTawUnSChUjAxf5dWO2lkpCl0w0gRJ6awV0NkkbQEQZTw/7y2iMYUoM8+eoxnzsyMXBlv+xFTFZfzS61MwXBqqkQnMK0Xr6x0mSg7dIOEfpTQC0M+eWqCMNFMVmykSFeb17q0+xGb3ZCya84VwEYnJEwURcccfykhjDUbXWMYCWQk0/DK5n4mehudgImSw/XNHu3AKHYGJZpK1TVRoil7NkfHi6m6R6PRfDIlAt6tnHv36vVyO8BJfVESZdJAEqUMyVK481fM8P6+e7lFohTrnYD/8GqfqYrLw9Ple5rv/V5BfhCfh3czxn6eCF/63Bm++MxJvvbSNb5zaRPXktTKDpYUvHytwUeOj+FY1l2v9N9L68jger681mGsaK6HH91o8uFjY1lazl5qhtlagWvrXZbbQXaPzlU9Tk5XRs7tbjwoRm3/dz5ymBcvbfD9K5toNE8dHfvAKGje6zjaHDnuhL8MKrMcOXI8WORERI6ReJBfOPs93M7WCrT6EfWicQhfbBgTRy9drQJTQG8GIa0hY7xBMexHiv/4xjJ/fmmDx+arlByJGFEqK6AfKm41+nzs+DiuZVHxLGKljVojivEj4xXRCUwcp9aaki0puxZBpIgSaPQiXFsSREkWZyiF8VCYHy+h6NHsxygErhQcnyhxea1vkjGKDp3AtBNooBdpepHxSXAsQRDDXM3jynpvz2JfQ6bkMCkamo1OSMGxcCxNf0jpMXyctDar9i9d3gRh1BJSCmwJCLix2ePttS79MKHkWowVHRSaoi354zdX6EcJYaz2nBfA4fECnSDiL242uLHVMxGelml9uLHV59iEaauxpOBz57ZNFBe3erzTC/n6KzfpBTGNIbPOYWKl14+IUu+IWsFmoxPwG88v8DsvXefDx8ZvK9gWVtpcWetSKdgmjjBK0pSOiczHo+TajJdcgljR8WPGym5WzIdxssPYUwiBY0u6fsSttEiwpYmLLbkWSaJxLWiHKmuxGZAWw/Lv/Uz0pioefmTSSwYpJVaaIuLYkjhUNNI42n4Y00+jYy0h2OqGTFcL71rOvbvA7/gxtYJDJ0iwLEEYKxxbYEvJY4fu/FkxvL+bm13WOqFpaQKCSPHK9QZ+rIA7p3sM437GHT6Iz8O7HeNORMvZ+TpT1QKfeXQmu1YBXEvwxq0Wp6Yr77qIPQhxMriew0RR9exM3XBprcvHT05wq9Hnlz59YmSR/ViY2+4AACAASURBVOyjU/zr71xLY3sVsiW5vtHlZ/cwWb1bomR4++Hj/+zZmWwOHxQ8iDjaHDn2w90awubIkSPHnXAw57scf+kw/IUjhch+HsjZHxT+7tPH6AbmgbBgS2pFG6U1JyZKADwyW2Gq4mWFXRoAka0SJ9qQA34Uc3m9y2LTpzki+hIwLRRjRV650SBOV2W3eiFxorEtgR8pukFCxbWxhGkJ6KV+CrEyUvSNbogjBW0/ztogLCFYbvn0gpiZWpGPnJjgH/z0af7eX32Ij52a4vpWP2v/8Gxrl1WaIQikMK0ZNxv+vsX+MHqRRmlNpIzKQGCIh71uep2+rrUxgZyuepyYquBZkptbPv0wMT4diTJ+EVHCesdnq2daTvTuie9CN0h4dK5Gy4/wbIuiYyGFQKZRp4tNk/QghOlfV1pzZa3DO+s9Tk4V6QaxiUqV2+cXTGFVdCVherylFPQihS0FljQql4WVDm+vtPju5Q3++Pwy5xebxqdj+HiFMRvdkO9e3uDVGw1Oz5TxHItOYEwfP3pynDDRPPf4LM1+xJuLLTzLzCRMNEfGiggBW31T7Lq2RGMMV0uOIbWUNr4fbT+m48ecmjIeEc1+xJnZMl95YYH/+0e3OL/YZK297bcRxoogTjg9UyaIFZYwPgoaQJAmvBgD0VjBX9xscGW9R6xI40Ml33lng4Xl1rvueT88VqTtb5NBloR+mBDECUmima151IsuZdc+UCrH8P6aafSqJU1qjedIpJRc37p74mRwngbX0uDne0kKeRCfh3c7xu7zALcTLbcafaoFI53+8LExPMciSkyiz7slUQaFe7Mf7SBOLiw1d2w3mEOt4GSf054t6fhxNt9BkV0vmkjaetHhi8+c5PpGHyu9j6WQ2c8vXtq453nvhQ/Kd95+ODtf50ufO8M/+7kn+dLnzuTFX44HisG9PIzcpyRHjhzvBrkiIsdIfFCMsUY59P/q543T+2A1rhclpDYBoG9vDdAYQ8tmP8aPEqJEU7CNlFyZoAHTiqDh8FiBaxs9RPo7GkKlsu2mKi4lz2YrNcoUkJldurbEluYh37EkaEWidVqIShYbfUqe8ZD4TxdW0t5+TRgnaEuaZIXUSXLQoiHTNourG33j43Bwr0rAeAco2CYKNDi2IIj3Zw1cW7DVizg0RrqCqXEtmfpiGLZnoxvRDRPk4FjtgsSQO2jQArZ6EVKYBIuCY+IDXUvSjxVKaUjgxFSZw2NF6kWH711e543FFkrDVi80cZTSwo8SPNu0jiQabGlMPwdIlMa2BVIIHMuoQoIk4XtXtjgyXsxMMDt+zBMP1djsxdzc7LLZMwqGZj9iSkourXb5+KmJrP91sLo+KJr++999HaUV9aLL44drAPy/P1okSDS3Gn20ViQqjbEMY8quxUraxlB2LY6MF4mVWVX66ImxrHVoturR8mNeud7gw8fGmE79JACmKgU+cnyM1280WUn9OGxpXouVzsiXMG1dGaglXMdE0b5yvcEnT9vvaiV8WCIexDGbaQuKZxsT1q1eyHTF5R994eyBCqXh/YEhwmIFtYJFnOis1epucT9XkB/E5+HdjnEQqf6wKmS6WsjUPIPr+N3gIKujF5aaXN/s8aPrDYqupN2PoOSCTlvsdsV77p7TqzeaTJZdiu72o0o/jHn1RpM/ev3Wju+Fv/v0sez74l7wQfnOy5Hjg4r7qTLLkSNHDsiJiBx74IP0hfOFJw5nD5gD8uEbv/daJgX+0ufO8I++/jp/+PoinSBBpEXLoHaRAixLoDUst4LsNVtKfDVU2Wv44/OrKK1Rels5oIBQGcn/Zi9ko2ek48NmljNVFz9SaduAwLEEBddEGEaJotWPCGJN2VO0/ZibW32EEIwVLBwp6AQxLX/HVADSlgwzH1sKlNB3VZRZlkAnmliRqTjiOzheakzh1wsTFlbadIOYmZrHWMnj+maPomNxeMzj7dUu4T77UpBFmFacbR1G2bPxwwStIUiMckGn6SLvrHX5ub9iCpOvv3ITKQRVT7LeCekEyW0eFMMkiMCoJRJNJv+O0zaeZi8i0TrzFQHT9rPYDHj8sPGhKLs2Shmz01tNo1L5wdVNnnt8/rYi7+x8nc+dm91xj6x3fCJldCVGvWEIHK0Uzb7CsS2mKi7PnJnCs22a/Yhf+vQJzs7X+coLC1lR9/BshZevGRn9pdUOrm0xUXaRwhRuE2WPj5yYYLMbsNkNjYoFqHgWYaw4NFZkuRVQciRSilStoHAtQRS/+5Xw4QL/hfObVAo2SiuUFniOuT4dS3JqRB//nfZXcCwsITI1iWUJSq7DdO3ezNAGxe3gc+O3vn31nvwdHsTn4d2OcRCi5b30FXhzsUmrH2V+Lqeny0xWvNu8TuZrHs1eSGRuTHphTBBrPn168o7pLRqN2KF/Mvf5Vtfnn/7Hi5Q9m5mKS6sf8U//40VubvXohfqefDw+SN95OXJ8EJH7lOTIkeN+I0/NyDESd+tA/m6wnwvz8GvfvLjKC+dX8RzrNpf4J9MY0EY3Mv4NQ/v3bKOXKLtmNZ209SBWtxfReoiA0JifPXs7bUJrs8o/SNGUaXHfDROUUriWRdm1afZjgljR7Md0ApMIIgUEsSZW2hhKKk07UHiOJIx3p9cbKLU9n6JrWhnQ+sDtGaknZqb6kAJsS+yrrBioQcz7NfPtBgmdMEYA3TBmrRMSHXASEjPmsYkSJ6cquJbg8lrXeCc4kliZ8Y6Ol3jicB2lBX/4F8vZMVfKpFVkrQhD0EDVs5mpehyfLLKcqgSkIFO8fPTEON30vG/1Itqpp8HxySI3tnzafkyjH6G1phuaiEvPlggh6AQJri05OlG6rcjbfY/84OoWjX6ELSUVz6bi2QhM/KprS05MlvnYyQmmq8XbEhz+/Ss3s9SAkmujteLqRpebDUNY/VcfO8oXnpjfkVjwt546RCdQlFyTFjJR9giV4ulTkyw1faLYtBTZ0ryX6WqBw+NF/sFPn973fB3EFX26WuDphyZ59UbDRNhqQaw1idK4duqXYVkHdvMf7O/wWIEfXN2iXnKZrXqIVEHz3/3MQ5yZrR1oX6Pez17pEgf5LLuw1OS772zwZwvrrLR8iq4kUdz3z8N7+cwdHLe9UkneTdLFfriw1OT3fniTIFZUUyPdm1t9bCE4Nlnm6Ycms9SSuXqRsZJDL0zwo4SJisf//vNP8F9+7Pgdj935xSZX1nvIVOkTxMbItu0nJErTi4yRbMWziZXmxXc2OT5Zvqfz/CC/83Lk+HHEe/V5kiNHjp9s5KkZOe4a9yprvltn+b0SAg7VC6x2At5eaafxmJLNbohtCebqHlKY1eO/uLHJz/1fLxElijCVKIygFyi7NlFiClPPNbGR/RHFvwaGHSQSQChTzEtMa4RW22MMlBMiVUhULIEUO/c7IDAQILTOSIdBoR3GexMLw8V3J0iwhfGS6IR3ZgHstK1jeEtLCtQBFBEAZdfCsQRhHJMAfmp0aUwcJYSKeJ/9QJriIc1PJyfLKK2ZqRU5d6jGxZU2CpgoOzx5pM5YyeXSaocfXNtisxtQsE2EXrDLBHPQhjP4v0Fc5oeOTdDqR9xq+viRxpLw2KEqp2dr3Gr4aAumql6milhY6fL4fJUrmz0sIejFpqAxUZwQqYQTkyUeO2T6sndj9z0SJorZmsdGJ8pm6TmSRGkmyi7PnJk2RFKKYdn38Grsesfn8nqPetHl6HiJc/M1/uTCGl+crvClz53ZVgVdXGe25jFX81jtBCw3jI/H82+sUHGH2nyE6Y3pBjG/8pmH9j1fd2uYeHisyHcvr9MPjbLFtSRhrAhJeHOxOWKE/TGqFetXPvPQu5LcvxuDteHj8anTE5xfbPPiO5t8+vTkfTfufa/MCO8l6eJOeP6NFR6ZrXBxpUMQq6F7qsMv/4y5xoZbHaYqBaYqBZTWLDX9A8/nF58+zlLTZ7Mb0vZjXFsyWXEzPxnHMu07iw0fgflsvVcjvR8HM8gHkdySI8d+eC8+T3LkyPGXFzkRkWNP3O0Xzr04y+8uEsI44fpGj81uSJwoNjsR0hIcrhcIE0UQaV670eTZswVevrrB67daADtUEFa66h/EJlVAiO2Yz5JrIS2BrxKsIWXDfhhsM+i3HxYKC0z7hGUJ+qHCsmSWcLAbAwGGLcGRMk0CIGsV2A8WhhSJNXRDtW9M5vB4u7cJ7kBCgCFWKp7F2UN1zi82KTjGg8GPycbthmqXYHr0fhTm+JWkiXnsxwnn5us8e3aam40+7X6EBlr9iMvrPYIoIYhMnGg/SlLz0dvnLAc7F8b4bqMbEsYJJ6erPPPILH4Uc2GpzWo74IdXNwnjhG6oKIUxnu0C2z4c1YJDux9hxBSmtSCIFVIIzh2qjuwR310Q/NKnT/D8Gyv80etLHBkvZAaelhCcmCyhMAaVe8m+hyWvb690sm1Oz1R2FFTAjnus7cdc2+jSDWK6YcJMxWOtE7Llx8RxQiv1ijg+UeJLn3v4jgX982+skCRqR6TpXNXbs5h77vFZvvbda2mSjSBRKlWpWFmqzd1iuBXrbjGqUHs3vf/Pv7GCUooLSy1afkSt4PDU0RLT1cKOlo/7VRge9DP3fo472Nf5pSbNfkytYPPYofq++7zV6HNsskylYHNptZseG5t6adt74n60OoyKd15r+1zb6JEojUg/68GonabSWNwB7tbjYTD3wXiDe+5+E/D3gjw6MUeOHDly/KQhJyJy3Dfst/II8DsvXePVGw0EgqeO1vmFp4/fViRcWuuaXvdEsdYOKLqmMaHRjyi5JmJxOTVTeO2mWXEVQpAMtVkkGkqWROuEMDH96sb0URtPAs0dzRpHYaBqcFNzRK3Nan+iQWrTAhLEiv12LTCqimTIm+JOMxkUzI40LSUDE8LBnPb6+7v0tQQM4SGlWc2/vNahG5qWhtQPMdvnYE77IYsGBeJE0eyGLDd9+kHM26sdio5RPPSChO9f2WKq6hLEmumqR6JcU2BHyQ5PDEMsbRNPbhoXGSrF6zebPDRdzq5BSwpeemcTSwqqRYeSp9nsRsSJZrpWYK7m8eZSmw8dqdNKWza6YUKszDXz8ZPjeLbNTNXZ8b6GCwLHgm9eXOU/vHqLx+arJEoRK4vDY8WM/HpopkLFs7MowFG9tcOrsSvtgNmqx+mZSiYJHxRUe0V7LjX7zNdNy4cGbmz1QAqmyh4Pz5S5ttHjn/3xAv/yG5eze2938XJhqckf/OgWG92AomMxXfUIooSFlQ69aHTSzNn5Og/PlHl7tUM/UhQdi1rRSlMHHuzXy16FWtGR+5JA++H8UpPrGz0KjkXVs/GjhLeW2/Si5H0rDO/nuIN9KaW4vtFDCEGrF1FyLL76rd6+SphmP8qUDsBtpMP96iffTQ68udjk1FSJN5fagCGCE2XMeB+eKe/427slPu7l2D6o6yCPTsyRI0eOHD9pyImIHPcNe608vrnY5PWbDa5v9Kh4plB66fImy62A2Zq3o0jo+DG2hFrBYa0dpKaSpsCfqxVSk0cTg9iLTJqF8YC4vSyO09qp7ceZ34PrmFjLe4HCDNNPmYZB5KYfqUx9ESbbq8B7Fet3M/pg4d/MXyDQO2I2d+9LYBIv0AdTP9wGYbwolFJs9eJMxaF2sRq792wPmXeOQjfSvL3WwZaCG5s9XAumZ2tUCg5bvZBuaIqU2VqBetFBCEP2XNvsI9DYkiyycmCQKYEk0bT8iIpnc22jS5QoKgWbqUqBS6uG1IoSTb3ostUNsSS0g5hp4PJ6l/GSw8npCpWCzWs3Bdc3uhQcybNnZzJDyd2F06AgiJKEV6838WzJeNFmsekzWXHZ6IRsJIrJssuJyRJSSj55epI/em2JP3x7jShRHB0v8cs/fWpHATG8Gr7XSvKoeyyMVZokYtiifpRQLzgojFrk2kaPzV5EwZFUPTu79371Z7fj/wbFVC9NMgHBUjPg0FgBBPuqGz5+aorZWoHlVpCpBuZqJvr1QWKvQi2Kk31JoP3Q7McIITJPj4JjZd4v71dheD/HHezrwlKLgmNRcIyPznI74Nx8jd956RrT1cJtK/0HIRmGybU3F5u0/Jh60T6wymCA3YX+2yttVtshJydLvLPepekbI9Zz81UmKoYIuFfi416O7YO6DvJUjxw5cuTI8ZOGnIjIcd+wlxS35ccEkXFWHzzQCyFY7wTM1bwdRYJjGQn7kfEiV9aM3NaWgopnY0ljODlRdllq+iYeMjWL3I12sL2Cm/k5cO8kxCgIoB2YFoWyK4mUznwqBq8PjzaY78AI8m5UBQJDuPQj88cjfDYRGOWCI4VJkkj2nssoSAGeY3qwlxv9A7WtDFB0LaJE4e/DRihtxtAYZcfNrS6PzNUpezZaa9p+zHS1gB8lFByLSsFhrBRzuF6gE8RsdMMdBbFK92kpTZwowljR6od88+Ia9aLLattnvGgzXikwWTbFli1NrGerH3Gr4fPMw8ZMcbpa4LNnC6y0+rx2s0mUwEzVGdkjPigIvn+lhWdLo0LQmk6Q8NSxccI42VG8nZkt8/VXFrmy3uVQvYDA+H38/su3ODVduW3/+xV5z7+xwpW1DsvtIGudiJXKCuSCY9HohVlca7MXUXQkBddCaSi6dnbvDRdKg2LKRCqa5BcpYHGrz1TV21fdYObb4+x8bcd8n3t89uAX0H3AqELNj2Jeu9nkyLghcQZtB7vP617S+lrBptWLMqIniI0kqZYWgO9HYXg/xz2/1KTZi7i40qbs2kyUXUquRceP8aOYF9/Z5DOPzoxc6T+In8Lg9+ubPY6Ml6gW7LtWDOwu9B8/XOObF9fY6IY8NFVGY+4nz7EIopjvL7fRaJ46OnbXqoR7ObYP6jrIUz1y5MiRI8dPGnIiIsd9w14FVL1oczNdpQLoBjGb3ZBOEBElmv/2Z06xsNLlVqPPY4dMlOJbyx3GSg79MCFMFN0gohc6PDpf41d/1hgHfvOtZda7MVGkDlRoHwR26huxnwfDgPfISIK0X+DIWIF2EJMosIQ2ioIhxsC2BEpvx28OXpHidmKhYBuPi2HfC5U6V+71Pk2rgsz4B9cSmXrgIMdGaUgSRdm1ibU+0DG1AIRRKYwVXewgphOOlvEDJEojMWqOTkoWdQOTLBLGiq3U6yFwjQw+SRRvLbdQWlCwJUVH0h0y6pTpvFtBgmvBRiei4FocqhdAw1Ir4PRMlY1uxHy9QKMXkmizanl0rMhSM+CRue35FRybz5+bG2lOOcCgIGj5EVXPXNNBbJQY1YLNUjPe8fdfeWGB9U5AdYiIQwg2u+EOMmC4GC45kjBOWGrGO4q8y2sdvv7KTcqebXwY+hEtP2a6YpRFmx2ffmRICCOQ0LSDBK01tZLxxvBsScuPdhRKtxp9HAv6kcKzLRKtiRNNO4j5qSN1zu0jS3/+jRU6QbRvof8gsLtQW2v7/ODKFpWCzaNztR0EyW4SYi9p/WOH6pQcawfxc3yixMk0mvR+FYaD4zisHDg3P9qnYbex6aXVLuudgMmKx4Wl5l0pDW5s9EFA2TXJF0tNn8myy1jZ5cJSm/HS3iv9e/lZ7CZ11tv+bT4bc7W9fUd2Y0CWtAOTdnN6pkzBlmx0Qq5u9ih7NqcmSyw1A4JY8ezZmexc3y3updh/UARBHp2YI0eOHDl+0iDvvEmOHAfDYJWsXnRYavrUiw5ffOYk5+brWfpBN4i5vtljqxcSxpqNTsBvv3iNtbbxfZiuFpgqu1QLNrZlcWK6zMOzVebHSoyXvYyE+Oq3rnB4vEzJFlnBLDCFsf0uruonD9eoenJff4XdMZKuJSi7No1+TNWzCaKEth8zUXYoOXKbuFB6ZGzmgJiwhIkKlcIkQQwwiNOMEo1tbf+fJW6/gePE+BIkSlGwJSeniiMVI7sx2CRMoNkPsYTx1bBlGr85NJYUUHakGd8SVDyLkmtxYqpM2bNxrdv3O0CkwLFS3wgFr9/Y4sJii34Y8+h8haJr4ceKOFH0o4Ra0SFSEClNO0wIE71jn8OHM0zAsgSVgk0nTJivmzaPxaZPsx9SsCXjZY/Pn5vlE6cm+fCJMbZ6Ec1+hNKaZj860Er+c4/P0uxHuJbEj0wkYRArTk+XRxYgtxp9gni7dQIMGRDGKiMDBsVws28IE8e26EeKX/r0Cb70ue0WioWVLh8+Nka96NAJE+pFh0+cnOBDR8d4+tQE690IS5hrslpw8BzLRK5GiomyISKCWKGVWaX+1d97ja+8sIBnCc4vtpkoO9iWpOxa1Ao2tYLDcisYeUyG5/zoXI1z8zWqBed9c/IfnJfB+XxzsYXGrKAbzwpTVA9aAwYYXnHfvd1zj89iWZJz8zWePTvDufkaliV57vHZ28Y76PWzG4PjeGWtw83NPq1+xPWNHlfXO3z1W1e4sLQzfWQw7tX1Dj+8ukWzH+FIyVzVG7n9Xnj+jRXOzFYyg9hW35DDK22fuarHVi/i3KHqjr+500r/7uu42Y/404tr/OhGAz9KdvhsnD/APAdkScuPs7998dIGi02fWtHmkdkq0xWPC8sdLAlhovY913fCvZzT+3Ud3Al7fb/m/hA5cuTIkePHFbkiIsd9xV6rZAOPiM1ugJ8a3xUdScGRnF9qsdULee7xeZr9iFduNJgqu6bY16bsdCxBJ5XlDwqHxw/XuLrRpVKQCGH6ucuexXy9wBuL7QMlS9w2z1ttPnV6kleub5mCPtG3eR/sVgr4scYJTbRcO4iRUhApoxLQbLdjDKIvYacKIiNSBFhCkgiFEMIkdGiwLbCkNO0HyfbfWKkSI9E6U0GMl116QZyZWnb8hPGSQy9M6Ed7H41BJKbnSFbbAVXPZrOrMtJl+BgoDb1IYUmoeRa9MCFWCa9e3wI0YsjLYpSiYqhrBj/WSAGTFZcgho8cr/HUsXHOL7U4N19jYbWzI3I0HuF7MTgWsTaKi8mSS6w1Jc9GSs3Caoc4MX3kHzoylhlAerbNXz09Sb3o3FVc36Ag+NpL1/jzSxuMlxw+dLSOa1tpAeby87/5UhY/OVV28Ozt1gkwZIBry4y0OGif+SCtYNh/YRCJ+M9+7km+/c4GMxUXP1ZsdkOSQGdE1ltLLWxLUHRsHFvyyFw1KxYXm76Zb9XDrUnWOyFBknBkvMh8vTjymOyec5QkXF7r8A9/9zU+f27ugRMSu9sFokTz8VPjmZkijC6k95PW36kF4X7EPQ6O4/mlFp4jt30aWgFn52u3XQODOf3jPzhPomCi4nB6usx01ZzLgyoNbjX6lAumrci2JGXPJoxV1kb3V09P4tjWjr+500r/qOtYKU03VszVb/fZOMixOTNbYWF1Oya07RvvmoGXTMGxUFrT6EWZUgXurT3iXiI8H2TsZx6dmCNHjhw5fpKQExE53nMMIth+56Vr/PtXbiKEeYicqRbY6oV4tmStHfD9K5ustPps9SL8MOHIRJEbW30EMFV18VzBV791hU5gVmClcJiueCw1+8RKkyhNxfOwpKRow17PuaMIigFBECnNzc0eWpt0izQhElsKolHGDCl6oVkZd2wJGmZrHrEysZSC7TYJKYwCYnhXWQqGgkApLAtsKSk6mkQpogS0VsOWDyggHLhnpnAklFyLk1MVHAk/utlEazg1VeZWo4/fDACjctjNSSjg6HiBqmfzznqXomtTjhM6gRpJJri24Nh4kY1OiJ8adVpSgzDv05VDFhXaRI/uRsmRuLb5Z0mJZ0surXb52MkJVlo+aG08GGxJkiSGkNApwZHuW0IWw2p4GU0QKzpBzLX1Lv1IUfYkp2bKXN/0ee1Wk0rBZn6sRLMf3fOK4tn5Ov/L335ihwzdSPtdvvbdG5Q9m5mKy2qzz5u3GjipAeR0xaHk2mx0QmxbcH6pyVdeWOD8UpNH52o7xhhVSN1JBj5bK9BKXzfjBHT6cebN4UeafhQZT5Oh1ePjk2VW2wGkhqDHp8qcni7j2lY21m7J/fCc1zs+L19r4FpmoGY/4jeev8iheoEg0e9ZpOFuDBdqX3lh4TZ5/qhC+k7HdL/i734UhgMipOPHVDxTrA/aZ/Yqps/O1zk2UeLjJyeQYlsjdDfF9+GxIt+8uEq95DKbEmQDkni6WrinVoDBexm0jLT8iH4UE8aKq+tdEq2xhKDoSI6M37l14Vajz/GpNCZ0rUvHj9FoJko2lpSZd4drCbpBwumh1Ix7bY+4l3OaEwQ5cuTIkSPH3SMnInI8EJydr/NP/vYT/OhGE1sa0zyAm1s9+mFCpDRX17sorfFsQTdMWGz4uJZAadjsRnz0xASubXGr0aftx4RxQpwoHNuiIEzrw2o7YL0ToPRog8aKaxHEyb4pEFc2etiWMG0JQhIlGq33JiHAmG/GSlNJVxZbvdjI4lPiIVY6M9Yc+GUOVBEDX8mSbYp4hSCIEw6PFVhpBYAiVtq0NChBuAchMlVxmakV+cSpSX7/5RupuV7CheU2/TAxZpaC29pDBmXMejvAtiRHx0skyngE7B5pMOcg1mz1Y3qRomAbc0xLGoPDONGEyihewsQkXpCYEzJQiKBNcdPyTZGSaHOcXNuYlc7WCmx2QibKDjNVjythz/hLiJ3ztqRASoEjdJaQsdoOUuLHvNEwVkgpOTFVYqnh8/L1Bv/1/Lv3MRhlcPiP/+A8Zc+mXnToBjHtIMGxLLRW2FJwdaOPa5vi/8kj4xybLNPsR9zY6FNyrB1Kh1GF1O7i8Np6l4WVDkcni3zlhQWefXSKr333hnnds7jZ6CGlYLxo0fATHMeQEX6k+M47GwCcmTMmk4fGClQ8s5q9u/Ac5aMwPOfXbjSNv0eiKLoWK81+qoAKeebM9AONthycE9cS5v6ZKO1bSL/fvfcDIqRSMG1dA8VAreDsW0wfHiveZlw6V/V2qAL2w3OPz/IfXr3FeNGYxQ7iZj90tL6nGuSjJ8Z4/o0VfuvbV0eSS4fHilxd73Bx44UotAAAIABJREFUpYNnm5QWgSBKNFGizOehMMThTNXbMZ9R99Pg2AxUTJfWuiw1QWnByckSG72Ijh9TKzoUXRvHMuqI3D8hR44cOXLk+OAjJyJyPFA8dbTOH59fphcm6cOpKXYLtqkwW35MyZFUPYt+lBjPAduslk9XCyitqaXO65fXOiRa44exKXiFQEidpQOUIIsw7AbGONKx5L5mimBqZqmMqWSSrsCPslkYFPZSwETZIYg1YyWHimdWvCNl4kUHLQrOLiXCcGuGI0GlDQ1lx6zwbXQjumFC2bWIVQJ6f1XGUiskUh0eninTCWKUUnRDI2ceEB5Km7HGCg5RnNAOFVKYOQSxkfgfqnl0/HgkYaG1UW9oTNHx5mILS0DFswkTRZQMKyg0rgVhpFEYokVIiZW2k0SJIk40QgwiWhN6EZxfbDJVdrmYtuzM1TymKw5rnQjS1dSpss1iM8S2TFH/yGyVy+s9bKHphopEm+vAswAh2eyGHBkvcXKqxGonzMwkdxc/Z2bLLKx0jUFeP87MF+9kcHhlrcOX/u0y1zZ7jBVtbCnY6oXY0hy3Zl9x7lAFtGa5FVBwbCoFO1MknJmtcHGlw3jZ27cYPjtf57Nnp/ntl65zY7NHGCt+6lCVR+dqNPsR1zcjfuETR/lPb61zc7NHGGsKFjT6CUIapY0tzfFxLclrt5qcSY0cZyoeGvjelQ0EgqeO1jPi4CsvLNwmuT8zW+H1m01eub7FrS0/O49Vz+b7V7eYKDs7evbhvY223H1OjIRfE40w/dx9TB+UtH4UBkTIXNVjYaWTJp5oTkyW9i2mTRrLTuPSpUafnz2gN8HZ+TqfPj3Jm4stOoFpx3jsUG2HCmZ4pX8/U8/BNs89PsuX/t0yQJYyIoSJOXYsyfHJEkGsaPvxjs/Uvfb92bPT/MmFNTY7AQsrHRBQL9hESrOw0uGjJ8cpOOb74LNnpzPT43dzDvdKUMmRI0eOHDly3F/kRESOB4YLS02ubXTZ6EZZ3/oAYaxR2hTFvSjh9EyFlVaQrQZ6qXS47cdZYfj3/s0PWGkHOJZkomTTDZMsPtMuGJPIOFH0wxghTLUdKYUtTavEflGeu1sXBlvuJiQ04NkWtiWpFSS2bYwbpRQobcYahDzsZdFgS7AsidImUaLlJ8YoMlVTDKJI4wNkX/hRzA+vbmEJQTtQOJbY7mNIESvoBEau70pAmBVLSwqmK25ayIPY1cMy2MuAC1nvBEihiRRGZaK3IzqFNkfLEgItEoQGLczvvTBhquKw0Y2Q0vSMWxgzzvGSw2rb59yhOp98aIKXLm9xbbPH8YkSZ+dqLLdCjk4WOTdfz0iDQcFwaKzI969scXyygBCCIGoSJuYYBGkWaTtImK2Z1dVRZMLXX7nJQ9Nllpo+QghavYiSY/HVb/V2FFzDvfBrbZ+FVVMkWVLTj0z6QKI0JVfS8mMcW2Qxn36smK1ZXFrtZv4Fx6fK9KJkh1/FqNVngD+5sMa5+RpoTcuPWW6HHOoE2apxL9T8+t88x1e/dYUXL62TKMVWL0LHZn5SglamjaUbGMLjxmYPpTXHJ8t89uzsbakDo3wUygWLZj+kG6qM5EoSzUrLx5KCpuBd9+zfDUb5ExyfLFMvOvumoMD7K60fJkJ6UZKlZpyYquxbBA+MS5dbAS0/Sgm5CgsrXb5wwLF/8enj2T1wJzXIQXxMzs7XOTJepNWPMnJjouxSdCQr7TBLvjh3qEow9AWw174XVrqZH0akFFMVj48cHwPgjVstXrvZ5PPn5jLS4aDvey8chGzJkSNHjhw5ctwf5EREjvuK3dJoAQSJxrMEi02ft1e7VNPV836ksFIjR40pwIuOaYVIFMxVvWzl7Ox8NXMjHzx0aiGoeA7ltK+6HcRZS0aSKHpRglKmUHYt0zbQDQZFkx4Zm7kbg9jMIbuDDBqoOoJHD9WML4Ef89TRMS6tdakXbRq9iPgOLR1gCIpYqczUEsyY4UEyN3ch+f/Ze9cYO877zPP3vlV1qs697837TTRpSrIUXxRFGcPjOHGirGezi2CzwWBtBIsBPJsd+INnjOzs5EPkALMIBgG8gHcHWGGCQRDP7ibGeCaZGFEi2/HKjik7tm6WSLNFUrw1u5t9O/dT1/fdD2+d4jnNvpIULVv1AILI7nPq8ladw/o//+f/POnaTZQcGv0IIcxYyAAFaUwd48SoFKQAC03RETw0UyVKFC3fT6MzN4/w1EClIAkihdbGt6EXqiz6VGNIFACFxpZm5KQXKhxpoiVXOiZG00pfd2C8xOOH67y51GGtG6VFSZ2Jisvr8y3CRPH4kQn+lw2F2XDhcX6hyUtXG7R8o2QYKzksNgOEBY4UNPsR3SDmMx97CLiz+FlsB5Rdm7lbHaYr7m3TwHbAw0OmgecXmvzNuUUzXpKOYLipaqfuOYY4UhAnCe2+wo8MGfLajQZFx8JNk15a/u1Cv+3HzA5J1d9cavHnL9/AsiST5QJRnPDsCz1KjsyO2RR1Jnbx4nKX6apH1bN542aTs5dXWeuEVD2bm41+NiIUxkahsL/u0Q/NcdWLDmHNHemEbywyN/NROHezjWNZFCyN1oJE6ex+i5QGEd+Xmf3dYjOyZKnZ4y9fa/CfX5lntubxW08d4ROPHXzbjuFucTdEyFbGpXshe/aiBtnO1HMYjxyoj9wrZy+v0upHPDRd4edOTALGQ2Smevte2skwdDM/jI+ccllo+juSTHvBbk1jc+TIkSNHjhz3jpyIyAHcHznqV1+b54tfv0Q3jOmHMb0woWBbPHVinDebZo65E5gceRXoTLbupEaOFdfGkjBddUwEZmLiPo1cfI2nTkzy6Y8cB+B3v/Iat1oBSmn8KKLqFVBpwS0l+GnBFaQ/s6TAkQI/NuqCnQiIATYJaTBKhfR3rVDzwxsNasUCk2WHRjfkhzca9CO1p2zcwfY2w8D4UWJIhO0OveBY/Or79rHQNHGo802fSOnU1FGg0BlZA6lxJmYsQwLr3ShL3ZAwQi4MoxMqumFojDYBNoydxBqKEtMt1wPDz9vnkSgouRJbSmZrXkaWrHZDJtOYSYCpisdHTrmcX2gBbDmbDqao+swvPsQXv3GJ1W7I/nqRmUqBK2tG3VArOnzmYw9lhejG4qfjx1Rdi4Vmn0MDJY4tzc/TomjQMS1Y0szVRwk31vscGjPbOTRRZrLs8Mr1JsvtCBDZ6ItGG/LAtVjtRkxWCtk8+0CR4NgWjgXfubSG0prD40WCWHFhqcPp2QovX2/zi2dmAKh5TmbWN0iUubrS5cZ6n04QkSRGfWFJga0hUOZajhWlSSzQ8M8+doJ/+g/fw+e+/CqTFfPPwXLb5+Jyl3Y/AkEWVbnRR2G9F1FyLbpBZPaRGroqpbEsgWNZD3RmfyNZ8uZSi2+9uYrrWMxUCrT6EX/wVxcA3pFkxF6xk8nmbrFbEmS3+9t4r+yruiw0+pyerWx5L+y07ft1rjtht2RLjhw5cuTIkePeYT3zzDM/7mO4azz77LPPfPrTn/5xH8ZPPAbFFZgYxZYf851LqxydLGZy791s4/f/8jxBktD2Y6NG0KYbvtAKEAKqrp126Y1U3pLG4HGQItGLErphwsCUQSIYKznEiabZi1jthtQ8iy9/f54fzrfohzFgFA+9MMmUC4PutNLGg8JKUzqa/d0MN+yMjUoKKUxawXI7YK0X48dJZvS4lb/EXmAJwXi5gDVwatR601hSiXloPjpZZrrq8o8e38+Ll1fxowQF2ejEALY05IRrS2wpuNUJcW2BH6kRT4nN9mOJ21MbBQuOTJTMfoY8NQbXdmBQaafMTKxN0oXnWCAEcaLSEQdDbBydKDE+REZcWeny1mqP6aq74/15arbGB4+OIYVESsGjB8f4nadP8y9/9Qy/8aHDnJq9nUzxxs0WLT/OIjVvtQPafjwybtPoRbSCiKsrPcJEMb/Wo+TaTFUKXLzVpdGL6AQRjX5EohSOLVnvGeJiIvV7UBqCRKHTxUw0uLbgyeOTNPox01UXIWCy4lIvOrx4eY1b7YAgSljuhKx2ArpBQi+MsSzBdNXDcyw8R3J9vU8QK6qeTa3o8Or1JhVPcnW1TzdM0khYgefYHJ1wzc+QVD2bRw9U6Yaao5NFbrUCWn5M24946VrDXGdhPCUur/T44NFxPnh0nBvrfW42faarLlOVAq1+TKMXmftFm468MR2VHJsoceZAPXv92+27MFayee71Rc4ttLiwZGT7AIcnSriOlcY8wo8WO/zGhw4D5nvrSy9e4z++dIM3brYYK9m7/s77cWOsZPOd1HC0YJsRoEGB/3acw1b7e+LYGF99bTFbw5MzFbpBxFdenufFy6us9UI+dLTOQivkpWvr9KOEX3//AX7+5PSuz+VBnevG7wQwvkXTVZenHpq8b/vJkSNHjhw53i34/Oc/v/DMM888u9nvckVEjvsiR33u9SWiRBFECseSphOLJtEamc7FV1yz7VbavU0SNTL2MDBtbPsRUWwSJ1Y6Pkkq/7+x3uOLX7/IgTHjgi9FkcvL3TuK8n6k8GOFLQS2AMc2UYVbWDRk+9ab/HkzJHr097EilaVLoiQx5y/URmuGu0aozCx/GCuEEFsqJwZRo+duNjk5U8m6eFu9XiszOuErnXk7lF2PfqRQYTLyvmG7iAGpMUCQwLW1PkVHgi2JE5MQEQzFlUowMaSDbWjzgH+gVsBPjD8EQvOZjz3E184v0+xHWed9bqnD6X2VXd+fu+nwnl9ostz2+fbFVcZLDgfqLuvdgPmGz3SlQLMX0hKC1TS5w5Kwv+byrYur/IOTRiI+IJhcW9IJEhZbIcvtkIItKXs2tpS4thmDSBo+UWJGhQxZI/jkU0ez4xwoElY6PtdSdUScKkm0gChRXFnt8dSJicy7YaLscjo1uayln9+xssPcYhvXlvTTSFmlwLIEQRxzZLyEtAQ1z+HgRAnHsnju9aWsi315uYObzsuEieaDR+vZaz778VN3GHb+m+cucHVNonVCLzJjTwVLMFa0iTV3bfS3G4XWZkajKv3QCQRxrPEKo7qkqmuZaFh+8v0A3g6Tze3WfasUja+dX0Yp44vy8rV1vvz9a/hRwkytyMG6x0o35PnzyzxxdJxfe/wAbT/ma+eXOTFd2Xbbw+fyoAxFf9wJKjly5MiRI8e7CTkRkeO+yFHnG30mywXe7HYoOpbxfkAQJ8Z/oBvEtHoRZdfm+FSR8zc7mYljNiYAiDReshMmWdEqSf0clJH6z693eeTgGGudZEtlQMEyM+sKiCKVmRVuhapnE6W+FcMqBg2ZjwVDPxsctxBmf/MNH8+WgKDs2gRRTBBvT2gMMBh/2AoWqboAkwIRK6MoSNLxicG6oWG8VMjMHicrtpHgb4FBdojQt0dDVjrB7f2m5z1QTgTx1meTKE0nSJASLCmZrBRYavnG9FJk6Z0ZRPrfcjfmPTMVPnRsgnrR4ROPHeTEdGWk4Dg8WeTIZHlkf1XP5txCky88P7fncaLhAvTh/RX+/q11Xp9v4liCcsHCj0xcqiWgVrSZrRc5OVNmquJxZbXHuZttPMeiVnSYqXksNvt0/H5GYGmgFyRMVSx6YUI/NBGnFbdAnN5IVc/m9/7iHEcmShwcK1KwTGzpxVtdio5FN4izdSJVGthSsN6P+d1/dLsgOzZV4X/66EPZef/3/9dZYqUNEZGahA6uTz80JEjdNSMdP7ja4P1H6sw34qzQ+5+/9BIt36iW9tc9tN7+u+BA3WNuUbLQj5Ap6Wc8QWBfzb2r2frNCII//Os5ZmsuYaIz0uFr55dHXvPFb1zi1EyFxw4ZM8Mv+9fpBQnrvZCya/6pGzYrfe71JZJEcW6hNRJ/+ZPkB7Ab0u2rr83zx2evsdTyN/XJGJAPb9xscmPdjFAMYmU3EjMb9/eF5+dQSmVxnZPlAq+s9YgTTTfsZISdJQVvLnf5mSMTWxKJO53LgzAU/XEnqOTIkSNHjhzvJuRERI77Mn97cKxIFCdcWu4SxCYysu3HaK3phwm2FFiWoNUPWeuFlAoW+4o219f9kWI92CRZc1DcybS56ccm836xHdz5YkzHXgPj5QK9IKaXjhpsh4I0yR3DpIMEXEcSJgoLKLsWnSC5Y1sVz8KPNGGi8BybiVKBZjdkoCHYiYzYgSPBsgQ6NddESDxH49oWQZwwXnIQwoxTuI6kXnK42Ug4t9BiueXvSMBsPL52kIwoQmwBUogd1R0yJVOUAqUU8w3/9u/E6IjKIMZUCkhixUKzz9XVLr/z9Glg82Jn4/35w+vr/PBmm7eWeyYmMjJmjhsj/DYjJwYKoChJuLLaz8gmk6wiqRXtNHZV84nHDowY5J3ZX+U7l9ZwrJiJkinob7UDpDTKCI1gvOTgR4pOEOHYFh0/oegI2n5EN71/Gr2QVj/iyeNG4bDUClBas9IJmKo4ZpswQm4cHvPoBPG2BVnNs3EsSTuIsITAtg0ZGKZM0Hyzj+tYWWF+7mabj56eyd7vOhbTjpWZYL50rcGpmcpI+gWMkgW//sHD/NnfX2O9F5n4Ts8kMCw0fRy7uf2Nswmee30JpRTfv7LGYssnThSJ0uyvF/mVR/cZ0uHrl+5QyUSJYrHlZ8f6M4fqfOvNFTqBRilFO0hGzErfuNnkxlof15FUXIsgSphb6tCLto/3/UnCV1+b5/f/yzliZe6lS36H3/8v5wDjkzF8HVup0ubCUoeKZ2eJLtsRM/ONPgtNH9eWeCmBFqZfoInS2LbITImHv0L2QnQ/6DjNH2eCSo4cOXLkyPFuwl789HL8lOLpR2ezRAqldfbnpzfk0Z9PO9Cf+/KrfOH5Oc4vNEe2IaXkfQdraG0KH9ex8Aq2IQYEzFRdxssu5YKNHytqRSN53wkDrwJLSoq2cf5r+7GR828BKQRRrOiHO5MQAKs94zdxcKyYfSgSjOllrFLFAYKDdc8Urelrqq5NwbYoORZag2MZgkAIcV8+XFVXIITAsQSubRErQyz0w4gwVqx1I9a6If0w5mcO1bnZ6NOPEta7Zub/bqZDRsZONCilU7XH1hjmOwZE0OC/UsHCkam6JFVyDDr9jhxEfm7tpDG4P6+sdDh7aYX/+IPrfOfyGq4tmCg7BLFi7laH9Y7PF79+iWY/GpHaD9+nYIqnqmdz8VYX15Z0Q5O2EsYKPzKeBxXXSj0TRhUlnmPz4ZOTTFZc1nsxrmMZJYA0vidhrGj1IwSKMNZ87PQMxydL9MKEtm8IOc+RaGC9F3HpVpt60eHwRIkDdY/JiksQg+dIbAsKjsR1TKfZdeysm78VHjlQ59ED1TTNJI3UTNMsKgVJnGhjZulHoDXrvduf8+deX+L0rCniB2QiwNxS547vguFxrrVuQDdM0Kk/yES5wFipgBBiW0XOVji30OSV6w3m1/tYYnBdEm42+6x2AupFh1jpzJB1gMlygbXu7SSS98zW+ODRccquza1OSK3o8C9/9XSmBmj5MQjjVSKESD1LyEbH9oLtvhvfTuy033/7zcv0ozSy2BbY0oyu/dtvXgZGr2MnSKh5Nq4tuXirC+xMGBwcK7LaDbN7Za0XAre/H6UQhtzdwIfulugeECU7fabfLfhx3Wc5cuTIkSPH24FcEZFjV3LUneaph7fh2MaVX2jNpZUuMlG4toVrW6z3IoqONIVFw9/mqEYhBZQLFlGiOT5R4MkTE7y10t2y0PYjM2YxgC1MUb3VtpUGPzYS+KlKgVsd80CttHmvJY0qQWtNwZYkynhABHFCL0youBYfOjZGzXX427llEqWZqBTwo4RYaRKls07hbiAw4yWOZaO08WuIYmWSFSyRnbcfm27jRLmALSX9SOFagsWmjx/trIbYDRINrc2kKruAFODHCeMlm1ud2Ei1pc7SOhxbUixYVFxrW8+HXzozzRe/fok49bNwLEkQK/pp93q9G3B1tYtjSb5/ZQ2l2VJqP1AAtfwIS5CNSxjlh6blx4wVY6qunfkxDM+LD5JbBp+HS7daNHsxiYaCZXxRmn1FsWDxqaeOAvDJf/c9HEtTKkjWumaMwbUtXrnR5D2ztVRBEPP5X3uYZ194i0PjHj+4uo4lJZY09/6gm79dh/jpR2e5ttbjyESRVj+i2Y/TYlDjp4ofS8JSO+DgWJEPn5zM3juIg6ykJE3Lj6h5NvWSc8d1GYxzrXR8fnC1kcnvo0Rzs9FnquIihaDm7f2fmGY/phskCEzka5C28+MkyWJKJ8oOq91w5H376x6tfjziLzJedvmj33rvpvdVvWjT7IVZ8kgQK7TW1Iu7P+bzC02+dPYq30q9RgYxwxtHGra6Znvt9g+/fhCJfHSyvKXHxY31XkpqGaLAtgRe+nMYHcureDZBuhaDaNmdCIOnH53lr99YzCJz+2FyW1WWjhQJIdBoSs7eE1TyOM3b+En3NMmRI0eOHDk2IicicgA7y1F380A4vI3BQ5MfK66udtGYDqYUxpiw4lo0+zFqm1q5kHbRE2UiPpPU6G+q6rLeC413whbv3VjyS5G6Jm6CYcXEjU3IEa9gUbAkQZxwoxlkxo0i3VPRsYgVXFrucni8xMGxEuMlmzDRnFtoUXYk3e1OdPg4gX/8pHH0/+aFZZbbAa4l6Eem2+xYhpBIMMVpwRL0opj1bsjZy6um4G8FBLEi2m1G6QaIof8PVA3Df98LlIYwgZVuTEGmxERye3tBair6o8X2ppL4QeH1X16dpxsklAoWYaIoOmYMYqlpfCis9L5SWnFjvc/BMW9Lqf3AkK5gSRYa/Sx20pJAqmRZ7kR84n37+ORTR7ck6AbEm+fYtERM2ZEgBFFiNCGPHaplrzWKFhNTa8oyQZgoui2fr59fwo8UE5XCyHb9WBnjSqWZqRf5raeOcGK6sitC8E/OXuXbF1exgiQlzcw5GjNYkwowUS5kRAncJmimKl4my984EjOAawlemFtmvtFPUzkspBBZakonjPnQkfE7Rjp2g5pn0wtjEq2zpBitjEJpOTWa3FfzsuSEAekgpeQzv/jQyGjOdvP9D++vU3IsFltBSro4HJsscWxqd8c8+I67vNxhLCUvXrne5ANHxqgXney7casC8pfOTN/hc7FdYblxOy/MLdPxY/bXPaRwNv1OdiyjghkKgUj/boiJ4bG8k9NlXrrWIIgVNc/OlHHbEQZn9tf5zMce4otfv8RaN6LoWCil6YcxCrKxjPGSzbHpCgtNf0++C3mc5m3kpEyOHDly5PhpQ05E5BjBVh26vT4QDh6aHj1Y48Z6PzUAVFn3ueZZ6B2MB1RqLKAwHEKiFJNll2trPX5wdX1LEmIzJPcQYVGwBGGs6KbumgNKYWDwmGjNsckSS62AuVsdyo5kvtEjTIwSopEkSDGaPLEVFPDdS6tMVD0KtkQI6McKS5okgCiNBQUI44RYCUBS9UwU6mBcoBvenYJBYMgCIaDi2kaBoBS9SN/VmEd2XhoOT5YAuLzSu21EKqAbmvXZKOMfFF5KKUPI2JKWr4mSJI0YNcqKetFBpTSJZ1u4tmS9H3F4vGS8RJo+X3h+jjduNmn5MfWizXTF5eCYx9xS23hDpFGvUkHFtfAKVpZqsVNaw8MHahybKPLqfCsbaXj8SI199VL2niOTZVp946FwPdH4UUwcmzW9ttpjvOywv+ZmhehnP35q03X8wvNzuyIE/7dff4yvvjbP5778Whrhaa6uMT3VhEnMS9cavDB3a0RNsZvEgPMLTW42fTp+TJwopCWy63FgrMhY0WG9F2NZ8o6Rjt3gkQN1XrpqYh61Nt4bcaIR0hBXzX6EZUk+87HNSYdP7HI/5nx7nNlfGznf3R7z4DsuTBRV1zb3EXBxucuTxyey78bhAnKl43PxVpeVTsB331rliaPjuy4sNxaiYaKouFamEoE7v5N/9tg4L8ytIIQhwoJYE8aKj5yaGloDc80nKy6nZirMLXWol8x+dkMYDBvMvnGzyfmbTXNsjoWd3huOJe8wydwN7od/0U8LclImR44cOXL8tCEnInJk2E76eXCsyFvLHRbbwYjD/FYdz8FDkxQOZ/ZV+N6VdUAjhWCiXGClY2IOB7P5mxXosYaCFBydcFnvRkSJQqNZbofsdepgD1MRd2CttzXlodI0iIrnsNQKCMKYjq9v+yAMvW63eGu1x82WTxQrpBRIIRgv2ax1I4aFHVGWo6mJYnAdnY6LqJF97wUDcgUNjX6MLe5t7YZR8RwWmv0RZYUtTdznUjvGjxVfeH4uI78Ghdf5hRalgvmqipXCj1RGmMQKOn5E0XUYL9ok2ugNwkjjRwn90JjnvbXc4cZaHwQ0eyElxyKINWNFmzABz1Hm/tIwU/X40LHxXXWlm/2INxfbxAoemq5kEv/FVsjRoc76bz11hD/4qwv0wgTXFqAt2klMwYJEK1r9mIVWsGNqw16KkbmlLpPlgomzjWKEVtm6F4wdAv/n317m0HiJTzx2cNeJAc+9vpSNA/x/c8v0wgQpBGGccGXVKDiKjsWTx8d3c1vcgacfneVP//4aSmnc1E+jn5JVGkYK5N2SDpvhXhMSBtei5hnTUs8xJNhK2+eFuWWiRPOF5+c4t9Dkvftq2RiLa0smSg4/Wgz40WJ7xBhyu8Jy47WveQ79MKYz5GmxsUj/zC++h5VOyJXVLp3AKGEePlDjM7/4nk3X4Ph0hd/+hYf23GEfJuz+1Vde4/tX1lnrhYaAnCjx0FSZuaXunq/XduTYgzax/HEjJ2Vy5MiRI8dPG3IiIkeG7aSfp2bLfOWlG5Rdm6pr0epHLDT6/MoW3cPhh6ZQwfGpMo1eRKI1hyfKWFJQKzosNgPiJNy0UBfAkYkSkxWXxdY6aFjphMZ8j72PCbxd8GNFN4jNjLzSKGW6uErvzRdigERDL1Q4EjzbohPELHciI1PfgtFQQBgppBwdp9gKG9UZW63nNomde4IUcHW1S7MfZwkaGtPVjZWiYFkImNCnAAAgAElEQVQcmSiNkF+DwqvlR1Q9i/mGTxApE5maEiSOLfFsyaHxImXXzPw3ehEIjedYeLbFWMlisR3gOsbZ348SFlsBfpRQ9QpIKXBtmSouTFH3yaGRhWEMJ258760WLT+i5cdEiWIyHa2A0ZQQgBPTFR49UOMbF5bRWjNWdHBth0SL1MBT7yq1YS/FyHyjz4GxIuu9kPlGwrBIRmnjuYAQ/PHZa1mnejeJAcMk40dPT/O352+x3AmN74c2kblBnPCjxdZdz7BPV1yurHZp+TGuLTk2WebEdJljUxU++/FTmWnfvRah95KQMLgWJ2fK/OBqA4BeGKeGmYInT4zT7EdcX+1nIyCDdAk/SqgWjYri4q1uRkRsV1huvPYnZ8qcvbRG1bO39F44s7/O5/+bR7Yt2O93SkSYaH7l0X0jBrRK67vq3G9FFgEjhOCVlQ6f/dNFDo0XeeRA/aeSlNitYilHjhw5cuT4SUFOROTIsFO39QNHxrJ56nrR4fRsZcsu1/BD03Lbp+ubjvfRiRInZ8porVnrRrx3tsLZy2ubHo8AVjoBxYKFADPjLkQWFflOgRSC5bY5Tm3M5rEtgdaCMLn7KMBIGaNHkbrOi2T78YgEQDESQboVNi5fvWjTCWJc26RAtHdpTll05O6MOLVRcgxSMzRQLsjUA0RjW4K1bshfvnaTRGkuLrV57FCdth9jC8GNVpAZXKJTEiI1cQzihJW2D9rlZqOPJSVPnRhntl7iGz+6xfuPjvHajRZSaG6sGwJCCEHFtSg6Nu8/MsbF5S6d1HCvVrzTnHGA+UYfx4KXrzVxbUnVtVlMz0EpTTuIqXkODx+oEqRrMlBRzNaLPH6onpEda90Aoc3IjZeSJEGstk1t2EsxMojUbfnGeLMfJkOKCEmiQSvFjbXe9tduk+0OCuKpikekNELoLJLVTu/XV280qbr2iMJjsy42cIcB4/GpsknFEaC15sR0GSnNqMc7xbRvcC3qRYf3H6lz7mabpXbAbNXlZ49PZOTCqdkKF5Y6RInKIl+DWPH4wTpvrfRY6QS7MnHceO0dy+L4VJnZmrut98KDjqO83537zY5/eERppeNzYakDkBq0/nSaON6rgidHjhw5cuR4pyEnInJk2O4BcuCoP2zktl2Xa9g0b6HRS4sUweWVDtfXeyht5NYLzV4mZ99YHCuMb0B/qY1gIAbQdz12cLewASudr94MQpiRgccPTdDohfTCmDgxxfVusJ26I4g1rmVMOXdDDQhp4jL9SO1ajSGBThATK1BpsSoFxsRxG/JDAKdmKiAEFxZb+NvIJ8ZKNlJKysImjBVW6qQfp+MQloSlpo9CEyvNjxbbKK3ZVy/S8iP6UZIZF2b7T/9qElJCglhTdy16seabcytUXJuCJTi/0CaME1Y6JmbQlgIQNHoxharFdNXLZuy3Mmcc4OBYkW9euJV1tsEU9ZYQlFybnzsxmW1npnpbUTQomk7OVHjpmumeh7FhZGIlmK56t0dJ4oTPffnVLbvXuy1GBh4Ip2crXLzVYakdAOBaZCM+tpRs9oHaKZljuCD2I4UUAtuwKtnYjNLww5ttbNvKtrmRQPg3z11ACsHhidKIAeOTJyb4wFFDEK11QhZaAZ//tYc5s7++K5+MnXA/ZP2j1yLmo6dnmL7Z5Mz+2oga4OhUmV5kCKG1TshEpcAjB2pMVz0cS7LYDnZl4rjptf+VU++4QvRBdO6HSfNBDK9rSzpB8lNt4vigSaUcOXLkyJHj7UROROTIsN0D5HOvL21KUriW2FIifWZ/nUY3RAiJJUwqwcD00QKOThaZbwZpYWqSBSyRGt+l+9BAGGs8R0JapNpSorW6b2MDO0KaojzYoiRXSuPZkmNTFR4+UOXf/91VukGC5wg6u1AW7HQae0rO1CY9Yi9zKwqTSjD4M5gidTsSAmCmWmC9HzNecoxZHzH9TS5KpSCYrRWpFU03OFGapZZPy4+RQqKFIoo1odLY0ig6pCWYb/ocmSjRDRNKjoXGSP7BFLuxMkaXJs5TZJ3hoiPphtDyYzxbEiY9+qmvRKJEFtHaCxPCOBlJXtipYHr60Vn+08vzjBdttNYEsUnwQIgtO9vDRdN01eMDR8a4eKvDQhNma54xYzRzDSRKM1YqbNvp31iMbDWmMBqpa3Gr5bPei0DINB5WojUc3tCp3i7lYWAOWXIkYZyw0IypFm2aPWUUFoO4Rq2x0gSRgcJjs9GvtTSC89GD5nyGDRifOjHJdNVDaZO4Mxwzei+mffdTUbHxWnzh+blNvycH4wKD/VbTVArLkhnBcjf7eyfiQXTuh0nzlh9RdW2CWFFJ42JzE8ccOXLkyJHjnY+ciMiRYacHyAFJEcSxkSG3fDxb8vjhMY5Mljd9oP/e1XVKBRPt50cJfjr/rgVM14oEiabZi+jHA8M74wFwRwWsNe+dLXF5pY/nSGxX0ujFD4SMkJAlZmyEKW7NqECpIOiFJkHj3M0WK5295HrcH3iOcSHs79HNc7Nl3OxnMk1gmK26/NzJaf7Jh4/x3OtLfPn71ykBUg4SLcz7S47kwFiJgi2zWfpEKaJEU3Vtyq7FciegHRgzwjgZJHZYoDVX1nrMVD1sCcWCzWs3Bu83HfdEGRLLkoJOEJtxnm6Ik6onHEsSJTrt0gsKjvGUGCsX+MDREldWupxbaLHU8pmtefzWU0e2LZjO7K/z4ZOTvHGzRSdIqHg2P39yio4fb9nZ3qg0mq56dIOYbpjQ9mOKBYufOVDj3M02rm3xyAHTUd/Y2R108YfTP2YqLjebPkcnyzgWfPPCLf7Ty/N8+OQkn9qQ+vGvvvIa33pzmZWOMXstFiQP76/yM0cmRs7xudeXSBLFuYVWZkxbtAVf/MYlfu7EJPvrXka2fPojx/mF01P8r195HR3F5roLQ2CVbIEtBfU02nIzAiGMjQHtALsxYLxX6f/bGYP49KOz/OFfz/FyJyCIE1zbYqriZsqFt6NAf7eZNsLoOt9q+SwjzGfxQA3ITRxz5MiRI0eOnwTkRESOXT3IDh6iv3T2Kn93cY3xksNkuUCizSz45eUusdYULMmXzl7lX//6YwBEicK1BP0ooRvcnlEfeC6Olwr4UUKkEgqWNIkI4rZa3JGCqmehEGgsJisFyq7NzYa/YxTm/cJ2Nf3AlNCzJc++cIX/+vEDjJccIqVNDCb3bvi4jUflJsejSZLb4yuCnSNDd4tKQeLYhlQ6OVvl4FhxpNC9stLh9fkm19b6FGxjtGlJScuPqBXNTPv7j9T5xvll4kRxaLzM44frvHhplUsrvcz3wZKSfqQoORKB4P2H65y9vIYQgoIlaEW3z00KQag0OlJZd10rkLZR1xRsSRIlVD2HUsHmE4/tz87nreUOjX7Mz52Y5MnjE7T9mK+dX+bEdGXbQu5TTx0d6Wy3/RjLUlt2tjcqja6tdnnpWoP3Hx6j5FqcX2jzdxfXsCQ8eXwiGxMZvH6+0c+6+EmiRtI/rq30iJWm6Egur5gxJ7Tiz1+Z589fucnxqTK//dETnJiusNQKcCyL98xUEEAnSDLfhWG8cbPJjbU+riOpuBZBlHDpVp9iQW5avH/246e4sd7jf//axYz0cSyB69i872CNh9M1cS3BC3PLhImi5jmcnClTsOXIvk/OlPnmhWXiRPM3byxSsCUT5QK/8/TpLddzr9L/tzsGUaWxNiL9FA7+Dvdf0fBO8cvY6Zj+8K/nmK25hIm+b2TJYF1rRYf1bkQcmwjZZuoTkZs45siRI0eOHO9s5ETEuxx7eZA9s7/OVNXjY++doV50eP7cEpZIFQ1hwtHJEn6U8K2Lq5xfaHJmf53D4yXmllojjv0DzC02KLkFgkjhWBZn9le5tNzFDxP66bhGrWgjhTR+C2lhf221R7jD2MBesDFBYiN22o8iNTG0JVFizj9Ix0sEprhO7QDuCrslIQTGHyJWIERCP1KGDNnbpAa2NMRKGGuEFCSJkd2HiabqSbyCxaXlLr/xodsP+gM/gorncGwSWn6CHyccGvM4Plmm6tnUiw7nFnokWjNVNYQSQMm1KaSJIwXbGFjGiRnDef/hOp986iiLrYCVToAQwoxupGxVrDQWZH4cQepiGiUaJKx1Q2xJ5g0xPIYxt9Th9L7Knjvje+1sb3z9Qivg/YfHsujb2Zrp8J9baOE5o1/Jg/Gn3/uLc6x2jNFmxbUZKxoCb77R52Dd49UbTaYqLt0gYrFlvCDGihaLTZ/f/8vzVAsWCEnZtYySRGsqns2BunfHcbf8GASZB0aiNJ0gphtoXry8ysmZMlMVb6R4/8ipGV691uTvLq8hBRyoe5ycqYwYTN5s+kZh4Vr0w5izl9aYqhSoek52XTp+TKI0taKdKSWG/RbuZv034n6YKW5F3g6iTR87NJa9ttmP3ja/grdT3bEZdkNabzymKEl4a6XLSifgI6em75osGd73tbUeJVvgORZhojg8bkYzXr3R5Jcf3pebOObIkSNHjhw/AciJiHc59vogO9xNrHg2V1Y6FGxBks6GCyEYLznZ+3/7oyf4zP/9yqb7Xu8roiRCCDi9r8L1tR79ME7TBsyMudICpRVjJZdGL6DRj1H6/pEQcH8UA5ECHSv+7uJqKjc30JCNKWwHCci0YO6G6q7iSaWAINEcHisSJZorq13iHQibYWPBTK2ioDMYRUlZEBszMhEpzZRrc3yyPJKYMigO/8WfvYZlSY5NeVnBOpjxf/rRWa6t9Tg4VqQXRFxZ6fKjxRYCGC/bZtQmjWatuDYFR/LJdLzgc79yiudeX+I/vzLPobEiCMH19R5+pCgXJEqT+YtIAWhNogUCjRCSXpjwP/6D/fRCnRVRhyeLHJksj6xHEMc8f26NcwtNmn2TorFZHOBeO9vDr//cl1/dtCMvtCn0o0QxWTY+EZ0gQWltTA5LDheXQ/phQsGWFFOiQGPMRg+OFbm8HCCE8X+wLYteGBPG0OxFvO9gjTDRBIniA0fGmKwYT42NqBdNDKofJcRKcXPdB61xbIkfJfzgaoMPHh3DsSwOjhUzMnOmXuRXH53l/EKb9V5ExbWz6/eF5+c4Ollmf93LEkqqns3JmQqfeuroCEnzs8cmMpIGNi/k70VZcK+Kiu3I27dbbbER2+3vfo9sDJ/3VmNAmx3TxVtdLGE+d18/f4uKZ7Ov6u7ZXHR4zb97eYVmL2K27jFeKhCkI32Hxot89uOn7vocB/t6t4265MiRI0eOHD8O5ETEuxx7fXAe7iaenC5zYbGddZwbvZDFVoDWiiurPd5cajFWKowUwoOCO4vfFPDQTJkfLXTohAmWMLGOnUDja42jEibLLmOlAiudgErBphvGKK3StIq3ZVnuClqZ+LiCLUdiO3cKrxBAwRbYluToRIkrq138yKgQdkNIDIgEz5HEieLicoeK63B0oshbqz3UFmtkpe/beHybvTwGHIysOogVZc/a9B6peDarnWDkZ9dWu1xc7vKPn33RjN6giZVRbxRsY+gZK8ETx8YJFXT8GMcSPHKgNmJ8Ovjz4P77m3OLSAHL7QCl4cR0hdNC84OrDYSEODFnIoTm4X1leqEeKVI2GguudHy+e3kdxxJcW+0hhKDViyg5Fs++0LtvcvfNOvJXV7o0/ZhTMxUWWz5r3YhWP+b4VInZujHsDKKEUsEiSMdQpiou+2qGrPBsK4uFlKmaIVFGVVJyJEFiFC0DlcPF5S4F29pUBTBdcbm22mO+0acbxBRtyb6UFAEoWILX51ucmK5kRrYDMrNedDKFR30oBnXwPSOFk42eDJtQ7kTS3M9C/l4VFduRt/c7unInbLU/1xL3fWRjcN5RkmTxteNFmzdutka2vfGYllp9k1Bji2zUZ26pQy/avQvvxjVPNEhpCMaJssiib5v9e/PleSeOuuTIkSNHjhw/rciJiHc59vrgPNxNnKy4zFQLLLdDpBAsNn0SbRIkEqV5YW6FsVIBzxH4kenMC4FRTmBSNGaqLtfXfPzYkBBCCCJlio9+lODZNp947ABtP+b1+SbHJosst2E1DkneQSQEgJSCbhgzXS7sKi1jAJ3+50jBzaaP5xgfhm46z6I3YSKGCYrB6EY/vK3EiG3FfMPflKgZvHdAdAzDkVt7YvRjU1BaQvCDKw1+9X3Gb+H8QpMvnb3Kty6uItCs9yIWm31eurqGbUnCSOE4gjg2kaZBohEa2kGMFZp1qxZs2kHCR05NZx3qTz119I7u5KnZMl87vwxA1bVp+TETZZcPHBljumoKh7dWuvQiBSlZVbQlq72YN242R85nY2f89flW6m8gsaQpbvwoYbEd8PD+2j3L3b/62jx/fPYaN9Z6BLHi0QNVHjs8PjImcmyqkqkBmv2I7761ysnZKieny7x0rZFGsxqTy4pr8959VTpBgmsJXl9o41jGIFIKkSbMCBINNc/OxlYKlmCtE26qAji/0GSpFRAlmgN1j2trPVTqdvCzx8ZZ7UW0+xEInRVnf/TtKzuSB7v9nnlQhfy9KCrmG30cC1683DL+J57DiekS842Yf/LhY297dOUwtlJ3FB1530c2BmTS995qZfG1WussMnOw7Y3H1A8VSmlmqkWEuE0atPzdkwYbCXPXlvhhQi9MsvQa0vv8XvCgR11y5MiRI0eOdzNyIuJdjr3KlDd2E584NsFSOr8fJgqlwI9i/NgkJ/itPgVLZAVyokGl4wJaw7W1HsmQf4ItNFJIEm0M+PxYZUkEJ6bKREozW/foRcmeiv23G7Y0RXyQaJa70Z4MJgGCWKNVQsGRjJecoe6zRCnFYFJiQCJstmmFUTnYliBWyhTjGBXK8HuGSYyNP7NTr4at4EgTyXhltctXX53nT//+Gn6kiBPTjY+VUWjodNQjUgpHQhgZ1kNr85/CeFdYlimYPEfS9qOR1Angju7k184vZzGS9ZJDqx9zarbCZMXNTOrKrkMnCKgXHSwpSJRmtRNSdUfHEDbey2GieOL4OK/eaFKyjXLAtWU2RnAvXfmvvjbPH/zVBWxpoi2DKOHs5TVWOyG/+PC+TcdEqp6NQND249uxn8td+qHCdTRHJkscm6pk0vHzC02++LU3+cbcMmESmWuVXstfOD3NeLnAxVtmVn+y4m7a5X3u9SUOT5TYV3e5eKuLbUnQUPEcTu0ziQTNfkQYJzz3+hJ/9O0rXFvrEUbJyDjFRvJgt98z9zo2sRfcrQTftQRnL69R9Wyqro0fJXz38jpPnZh4INGVw9hqf3/07StMVkb/ed/NPbzdmgxIokFcJpBFZg5ve+MxlV0LzzHk3oA00FpnaSq7wUaCaqbqgYYgUVl6zdGJ0sg9eDd40KM1OXLkyJEjx7sZORHxLsfdPDhv7CaeX2jyz//sVXphQhglxi8BUxQnCnpKjxhCDpMSGyvqWIGOEiwpTKdxpsIf/sbjnF9oMrfY4m/nlhn4Rwxv8248Fe4nYmWME13rtspg+Ji2IyYEhgBAwHjRoRcqPFsSxwo/ViOqBQ3Ywqxdtm1ur0OioWxJ+rHKCIhBMT54vWWCFdDcJilsmUaR6u2PtR8plFYkStPoxwih6fgRsfGGxBoau/FskZqKpiuhSf0/biNMFFXPxrYktaLDH/7G49nvvvD83KbdybmlbjZisbFw+s0nDvHP/sNLpN6VhHFCP0qIEs319V5mojrA8L08GNWoeQ5+lGSd20paEA8K67spYP/47DVsKWj5MbYU1IoOvTBhoRVkqRWbKQHef7hOs29iQiYrbjZOsZWZ7Gd+6T2sdEOurvbw44SiZa6wY0kmyi5n9ltZ7OZ2HjBSOExVjM/H96+sp5GsmrYfc3W1a6JQbYv9dY8oTnjpWgOAo1PlTcmD3X7PPKhC/l4k+IOknGEMf9bvdzLGTthsf3ejLNlpTQYkkUk3ShBCEMSKA3WPF+aWiRLNF56fyz4Pw5+rKysdFltBpiA5lpJou8VGgmpfzeVmo8+HjoyP3HMbE2D2igc9WpMjR44cOXK8m5ETETl2/eC8VQF2Zn+dDxweY6Hh45OMeA8MHtgtKShIzW7UuImGJNF0wxihNb/7lde42fRp9QLiWBFrEGgKqczfFmTd9p38GN5uJArSMXykuL0G8nYtnv285EiKBSsreCWafqyoeTZtP0JKgRoiEARQdS2EFHSDGKUMAbFxkqIfJcQKChZEifFIGH7N8OjFgAsSQqDTda26kqZ/53yGBPwoyciRdhCNrLkC5ND6x4nGSs9h4AsyTHJojCri+nqPiutQ3yCrHhTFKx2fi7e6WSe2XrpdJGx277qOxXTNZb0XptGagnLBAiG2LTYHxc6+mssr1xvGuyJRzNY8rq52+Z2nT++pgB2MYiy1fBabPkVH4Np2lvBRdCRNP+b3/uIc9aLN9dU+p2YrI4XVpz9yHGDXhflzry/x8IE6Tz00lf3sreUOC60AJyUxtnv/xkJsquLx3n1VFlpBplY5UPdwbIsoSfjeW2Y8oehYvLXSpeBsvY/dfs88iEL+XiT4YaJ54vg4l1d6rLR9/FhRkIKXrzfuILq2wtttiHg3ypKd1mQ4wvlbF1cZLxkfmrmlDhp48sT4pp+HQaLOmf21kWPZC2mwkaA6NlXhlx+ZZW6pe18JqwepyMmRI0eOHDne7ciJiBy7wk4FmAYqBYuWHyEwXhCDwhtM2sJW3gNbwRFwfb3Py9cbqMQU044tcKUgjBVhoimmXXdbCJACNZRYcTcoOTIbadgMZvRBZvP2G6E0VDwLAtBokkhnZIwUoAWUHItukKDQdPzYRGxiZvmFgOmaRy80c9e9MM72JYUpsksFa1tTtsHhDyZXNnrCbRzNsISJu9SY4nir01cYBcPgfUF850oP/0hpqDgWvSDO/DzURgNOYeIhCxY0/XikkDs4VuTKSocLSx36YcxqNySIEqQQ/Lf/x7eM1wQii/gcvO/9h8f47uU1LCGplxwE5n45OFYcmWXfiEGx8ydnrxIkCktC1XMoFqwsQnKzaMLLyx3++Z+9yi8/vC8rKAejGGXXZqZS4FbLp9FPGC8KbMt87fbCBKVMIsaTxycoORYXUhO/Rw7Us8Lq/ELzjmPdChul5XOLLV690aTpG1XFL5ye2rZY26wQk1Ly+V97eMRQUhFnhoWD8YRG33gk3Gsx+CBSC+5Fgj8ga05Ol2n1I6qeg05Tg3ajqrgbNcZe1+RulCW7WZMz++v8619/LDuevzm3SMWzefRgjanK7fcOf8bul8plM4LqE1u89m7xoEdrcuTIkSNHjnczciIix66wU7csTDQfPjXFn79ykyAyJoGWuHuFggW0Q4WQplAPB7/QxkMiYzqEQApjeBjeIwkBEG4Tw+FIQAj21Vyurm1esJhi3Rj93Wj4SAYFvoVjGaO2Zj/CkhDGGimgGyQgTMpDxXVYbvusd0MsqYm1yNQelhQ0+hHr3XDTfW+GjSMrljmFjDBITwk0FKTZx2ANBkqO4XQSsYcFFgLiND1kWNUh0vMpSLAsI2fphorTs4WRAubpR2f57J8u0uqHLLcDZGrCGCaaV2+0ODzhMVYscPbyGoutgM/9yinO7K/zqaeOstD0eeV6A4nxzKiXHB4/XB8prLYq7qarHk8/sn9Enj2IkBwu1lY6Pj+42qBgCdCMFJR/fPYaZdfOtnGg7nF5pUfTj/EciyjR9KOEqUqBiUoBKQTHpiqMl13qRWdk9GQvReuwomFuscXfXVpFCkHNtWn1I/7gry4A8InHDm56zXZTiB0cK/LNC7cyw0JzrUdje+8WDyq14F4k+AOy5vJyBzdVt4SJ5oNH6ziWteMa7FWNcbdrsldlyV7WZLDt26M8t4dVftI9FR70aE2OHDly5MjxbkVOROTYFBuLtDduNjmz35jVLbd9Li53U/d882A+eIj96KlpXnjTzAtLAckmXfPdQGGK4EipETIjTKBgg1I66+Q7UhNEetPYyb1iu8M1SgHN9fXbD9kmZcGQIX765kRpKp7D/prGdSyzTtIUq4cmSpwQ8NZqj6YfobRGKYgjhWUJpiouUZIgJekYixlBwXgGEqfKhcFj/8bDvW06aYp9KQVRuoC2gIIjjZIhjeKYqhRoBzHp5AKWEFmqySBZY3gnCXeSG8P7Hj4moc2aOJZk3LMpFSzWe8bIsxfGhlSINbZlPDJWuuEd3ddD40VeXO8hpaRgCeJUqZAoWO9G7K+XCGLFD+cbI6qE/+6DB7mw1Kbdj/EKgof3V5mqmCLu4Fhx2+Juu87wcLF28VYX15bm90V7pKBcavnMVAoj27BSAuhWO6Dq2YyXbGpegamyw4uXV7PREykFX3h+jvlGn2trPfbX3F0XrcOKhldvNJFCYEnBVNWlVDBf93989tqWRMRg3XdSTfynl+cZL9qZ+WAQK37mcP2eC9AHlVpwLxL8AVnzL/7sNZRW1IuFTBGgtN5xDfaqxngnr8luyIs8EjNHjhw5cuTIsRlyIiLHCM4vNPmTs1f5djoD/PCBKs1+xI31PiXHouLZvHStgWtLHEtkcuRfOjPN184vM1svMltzWWgGxHuJjdgAjZHx+5HCtWVWTGtuqxaEgERrbMsmjO8tP34vGB4v0BjiYXjvQayIE8WxqTKLrYCHD9Z55EA9m4n+1B99j0Rpio6FGeAQ9MIYSwieOD7OD642ODJR5q2VDlIIlNY40iSIbEy7GIYAigULpRQgsCXUS2YsYGBumah0/CI2pqJKg9aGxIlijdAKtcX2B7BTv4eNrxn83bUE75mtsNg05nQnp8tUPFOoeI5lIiG16aKPlWxc2yJOFFdW+zx+aGxkm48cqPPdy2tMlEyBvtYN0dqQUGGi6IUxK52AROlMlfBvnruAFIInjo7zo8U2QgguL/ewpcSyJL/5xKFti7vtiqvhYq3ZD3EtSZBoHjlgSLpBQTlb82j1I+pFyUrb5/p6HykFntBU3ALdMAYkaz2f5bcCpioF6kWH5U7AahN4bP4AACAASURBVCckimIWWiG32gG2hA8dG+eDRydH9rERA/KwE0TMN/rcapuUECEE4UrCbM1jvOSw1PLveO922Ew58uGTk7xxs0UnSJDCKGm+f6XBRKWwa5+EzfCgUgvuVYJ/Zn+djz88e1eqir2qMd7Ja7Ib8uK515dIEsW5hRYdP6bi2eyrunkkZo4cOXLkyPEuR05E5Mgw6FxdXu4wnkarvXytyQePjnF6tsKFxQ6uIzeVI88tdbOH2EQL3newTqMXMHert+fjcC2yIpm0eW8PjRMkWlOwBXECBUtTdATd4Pb7ByTB25GksTEK0xyP+b8lYLrqoLVktRfSDWIeOzRG2bP45oVb/L9/fy3zBBhWbxRtYTr+tsy67J5jsdY1MZ5Fx6IfJni2NOkFWxybJSCODXETKqPGgDSCMjQpGk56AmEC9aJNwZYQCLTSmYnk4BxHznvIZFLp0XSMYQhMUSqE0VRIIVho+nj9yBBIWpMoo5aBTJhBECuiRPGdS6sjzvtPPzrLl168Sj9SlFKvhoGvRcGSrHVDQFCwBdWUWFhLR1cePThDlCheudGk3Y9oBRG/+1+9lzP76/zRt6/gWPDi5Vbm5H9iusR8w/gcbFVcDRdrgxGhDxwx4xxwu6D8hdNT2RjEUvt24X9ovEyxYHFlpYcUgkSZRJPldoAfJdxqBySJ4uUbLaqunSWPfO+tdWqew3tma5sWrcNd5/fuq/HDGw0SZdbJtSWx0lxb69ELCxyaKO/ac2CrbvYvnZmmHymSRDG31AFh7on9Nfeeut0PMrXgXiX4d6uq2Ov73slrshvy4o2bTW6s9XEdScW1CKKEudQLZS94EN4hOXLkyJEjR44HB+uZZ575cR/DXePZZ5995tOf/vSP+zB+avClF68BcH2tT6lg4VhGdt7yYx4+UOf6eo83b3VYaptO94mpEidnahRsyc2mz//w5FGeemiSRi+iVnT45oXlHYmAjTF4ljAGgY4lMjPEeCg5YoB9VY9H9teYbwb0w2TLuMn7jc2i+wbFnhCm+/yzxyZYaPj0ogSlNeduNmn7CaudgE6Y3HEusTIjF3XPRkhBqWCl4xGCWGmOThRp9CMKtkV/Gw8Lz5HUSzYJgnJBGtWC1vQiM85gSYGUgrFSAVtCsWAzViowVS3QDU1CxFbLOEzqDMbBR3429FqZjsz0ooQoUvRjU7CGcUI7SEi0GcXwHBMDGCZG2j9bLVB0baarHt+5tMrRyWJaaCi+d3mdMFFmHCfWJBpcR+KHJkaw6jk8fqhO2bWZW2oTK8VYyeHcQhtbCoSAtp9wfqHNeNlmodHnxcvrWOl6B7Hi8nKPetFmvRdzY73HjUafThBxeLw0UlxNVz2eemiSsZLNdy6ucX29z2onIE6MgepvPnGInz85zWytwI8WO1xb7aXXx0JKQasfYVvmuo8VHaQQ+LGinUZ7xkqbBBMBBVuitBlFavkxh8ZLWdE6ID/g9me3XnQQQvCtiyskiSJSOvXWMARSN0g4Ml7kP3zvOkstn311l1iRrffwNjfb7sATohsk/OYTh/jLHy7SCWImymY84cBYCYAb632eemhyy3t1K4yVbL5zaRUw594aKtI3HtuPG9NVj6OTRW6s97nZ9JmuurtSVez1fe/0NRl8Hn75kX089dDknffQd6+ZGFzXRghx2+xXCH7jQ4d3tY8BIQYwWSnQ8uMt79kcOXLkyJEjxzsHn//85xeeeeaZZzf7Xa6IyJFhIAGueDZBlOA5Fq4tafkRP7zR4Opqj3LBRgqN0oLX5lvUig4ztSKudXuu3bUEr1xv7MqocuNLEg1rvTR5Y+j3w7J/W5ou+1IrILzPDIS9Rad/p2O20djSdLffWu0RxAmWEMyv92kHMdWCta3/hMacd7VYwI9McS2l4GePjXNxuYMfK/xtDkwCR6fK/NKZWc4vtJitunzn0qrxf7AlxyZLPHZoLHtoP3tphdVuyMcfNuMif/HqPH4UkyRbqEiE8XywJVRdm36s8IfiNVzbEEcaCBLNejekVLAI04vYjxU6HQ+R6WtiZe4xIeDoZJFSwcZzrDtm4P/pP3wPSy2f/+e71/FTg88xz0IjCVTCZMXm54cKoELq23DxVpdEKda65n6qeBYI+OI3LnF8snQHoRTGinM3W8zUipzZXxuJGdxYJJ5faPK188uc3ldhoemz2g1p+TGf+dhD2Ws/8dhBTkxX+OS/M6M4pYJFkmiafkTRlpQ9h4pn02v5RIlZH8+x6IbGI0SmiSJFWxIKQwjWi86mRetG+X43iKkXbazAEF9RGqUaK0UnTO5QPG2VJrLdWMCZ/XVqng1a0/Zj3rzVRWuYrLh3PTawVYcdyL5f3knd8LtVVezlfe+kJIe7USXUizbNXogfJbi2ISG01tSLu3/8eFA+GTly5MiRI0eOB4eciMiRYTiW7qVrDcB01AuW5IfzLSZKDhXPYaHpY0uBg+AHVxs8elAbHwPbYn/do+3HXFnd+0jGMDZTHvz/7L1pkKXXed/3O+dd7977MvsMBjOYAQiQIEgRokhRXCRYcmSXyio7CVVKlaqU8geWiw6ixJUPilORy3GYQqqUihVXVBWGKscxFDmWJQsMIImmSI4gYSFAEEMMZ996eu+7v/vJh3Pvndvdt7fpnhXnVzU1Pd3vve+57317up/n/J//X6ALqrDb4VCbNyGcDbwMNmPtObdLkio8T3tZeLbEsy3iJKUWJigFy5vEbQK4li46b6xo/4ScY1H2bc7PN5irh6DY1IxTof0R/v0PZhgtevwXP3uC3/qlpwFdwK2Vdk+V/d7Oasm3KfsO87aFUhmpUusVJkp7S6RKYduSfCddI1Vdw06JIrsd6SlgsuIzUw1IMz2OEacKCUgJKrvt92FL/XrDJOOp/au9FgD++N0b/PsfzK7y5miEGT9xrMJoweODWw1c2yLrFMQjBZ1CcXmxSTtKeo8ZLXjkXYvFZsTlpRafODrMxYVWb269krMJkmxbxU5/YXRkrAid6//d84ucm232CrX5esBH9pV461q1422iSFNFLUkp+Q4L9YAw1dfHkhDEKUrp5la30VPsqETGy34vTWMta+X7Bc+mFaaUcw4HhrVK4cJcA6tjXlr0tOoGdMPmk0dHBjYPNhsLODtT5XrHuLXcaV6+dXWFExNFjo4XB65zO6wt0o3Z4YOR5HCn78Pp6Qp5x+JWLeyNQR0Zzfe+b/qff6Mmx73yyTAYDAaDwXDvkPd7AYa7y9mZKi+9eo4XX36Hl149x9mZ6obHvvCUNl9zbYuPHqzQjlKuLLaoBTGtMGa5FXFhvkEjTKiHMUGUsNKOuTBfZ6kZ6bQHIajkHOIk2/XNtdHO/HZJMr2rbO/gMfEdRm9kSkv/LUEnSSBluZ2QZPSMIjcjSsG2JPuHcthS6KJbChYbEc0g6Y1DbIQC6JhaTpX0nP4fv3uDl149x/szVf7ywiLfv7rEH717g//tP1zgT967RaMdMVdrM1MN2F/xcW2pjSvXLNa1BDlXYktBzrGZruR4bLzYK04FdAppgdMxfxBCsNyK8R2JFALHEj1vDdVRVjiWREqIM4UQgo8fHmKsuNprAeB3vnWRehCTdLwlukqBv768TN6zODiao5LTDbJKzuE3XjjJiz93gtGiRz1IcWzBdMWn4NmEScZowUUg8B2b54+N8qXTkzx/bJQwUYwWViddbFTs3FhpU/JX93GDOOE75xeptuNeofad84tMDeX4qeOjCAHVIMGxJK6lGw3tWFHyLH3tLD0O5XTSabvvQ5LpMZdfff7Qhu9/93u32tZJLCcmikRpiiMFWZZRbce04pSPHapoxVNHXdNVPG3kObD2ebsfv/DUJK+8N8vJSV1Mhh1vEoBzs42eMete0N/06f7/0lVwGO4dg96HLMv4zT98f9OfLy88NYmUklPTZb54apJT02WklKvukW6To/975198+1Lv+fYP5agHq5u5d8snw2AwGAwGw73BNCIeYbb65W4tXQlwJeew0IiQUvC5k+N86tgIcaaoBilhrOf5W1FGM1Z4li5OlVK8eWWFNy8v8vKb14gzncRg7eHrGaSS2Or4VG0eyblXWJ0/tiWptnVht1N1hS1hqRlhW5LDo3nGSz6y42+wFQJYbsVEacqlxSZvXF7kH/2b9/jWB3PYnbjMvzi/yIX5FlmWUfQtamHKm1eW+ZmTYzw+VebYWH5gwyTNtComzhRPTheZb4S8P1PTIySdk6edGFLXFuRdScV3ODZWIO/YtKO0l3TSfU+E0K+30PWpKHq8d6PGN384w5/8YIb/8MEcP7ypm2iXFptkmTa+FAJk54KEieKV92apthNeeGqSr/3yM3z1Syd6u8f/+BdPs38oRyXnknctgjglTDKmKz4fO1hZV2DbUqzbdd2o2BlUGJ2dqTOcX12oDecdzs7UeXyyzNHxIqeny5ycKnFisqzHXTrX43Mnxtk3lMO2BHnP5uBwDt+R5FyLomfz8YNDW0Zudr93Z6oBzxwc5h984TgTlRxzjYhyzuFzj48xVclzfLxAmGQEcUoQp7ide3ZQ82Dt81ZyTm8H/MZKm0OjBT5+eAjfsaiHCWXf5mDP22NvGNT0Mbvh956178NCI+BHt+osNaJNf75sdg912arZtFlDzGAwGAwGw8OJGc14hNnNXO3NlYAsU5yfb+q4v25iAqu9G+I0ox7ELLUiwjjjRwqKrt7VjTLYmS/6zrgbqRh3sgYBCAknJktMD+X468vLZErv3FuwLa8MR4Jr62JutODppIsg0QaSauPn0CkV2ogyU9qH4NpSm3ac4EpJO0r59o8XtOFhd2pCCGypd+WTDL5+5iqHRvLcWAkYyru0o4QgyXrHdxUdEriy1KbkaRm+rqQT4kThWJLDI3mSVNEME+zOLHg7TpGsV5pEKVhC4TuS0YJH1hmzCZOM5WZMwbUYL7lU2zFRmqFShW0JnaaSqt6YSpplPQXI2uLm1HSFr3zhMX77zy6w2IwYLbgcGc0jpeTLzx8GWCUF/8oXHuO1s/O9cZXN0gwGJR8st2I+fXxk1XGn95X47vkl3ZxqxziW6KTNDJGfs2lHWjVzYqrMcMHlT35wC9uSTFZyfPr4GOMln0wpZqpbR24Oku//5z/9eO/jfmn9Rw9WODtTZ6Wd8Jnjo3z5+cMb/p+w0VhAd2xjrOj3lCxrxzj2gnuZGmHYmLXvw/m5JkIIRoq3mwcw+OfLVqMlW41ePEg+GQaDwWAwGPYG04h4hNnql7u1M7knJgu8dnaeSs6hFcWstBIQkGUZqq/q76+Jw1QxsxJgWaJXLLfiDLtbhO/B65CCntFhpu7M+2EvcaUurLvnz4Asg5lqwKGRPEmaYkl6yQdiG6MZlhQ0o4QsA8cSXFlsEiQZ7SjZ1BsC9IhDmmZkCC7Ma8PAOFEkIuX6cosoXf0MYapYakbkHIllSWZrAc8fG6URJJR8m0xZtDqdAwn6HlBQytk0opQ4zbA66gTXtthXcSh4DlGaIS3BT50YQwrBu9erOu3DEXid7f9mJ0bUkuA5NivtmGOjBQ6PFnj6wBBnLi5S9nVBc3G+xaeOjTJV8rm+0kaiVRRh5+W4luDQSIGj40Wq7XhgAdQ1jHzlvVl+eLPKTC2kkrN55b1ZXnhqcp3nQvfYrYqdQYXRZ46P4tirNUCebfOZ46O6SOukqnz8cKVXuJ+5sNS55grXtpgs+5ycKq6an9+rort/zY0w4XMnJ3Zl+nin8ZUP6nkMm7P2fVhohDhScny80DvmTpUq22k2PQg+GQaDwWAwGPYO04h4hNnKaG6t8dhv/9kFTkzo2f+o47JvS2il9Crp7pRArwjvfFByLeJ20hufiDJwOykSu0V1TAp9Gw6PlkDAfK3NcjtZZWB4L9B+CPR2tvvPWw9iXjs7R9J5zY6lVQqW2NgnwpHdr+kr69swVwt64wtJurVJZRBnHcWCIuzzeLAUPS+AQUkfzShDiIyxgssLT03yf3zvMovNiKRPfpEBjhRMlX2WWhFDvm5GAHiWZKzooBB89sS49prou+cUVVxLkGS6aeA7kizLCJLOyEyWcWqyyFI76Um+G0FC0dPFfC2IAfj046P88bu3CDsqDSkVrhRMVXI8c1AXJpsVQN3i5epSiwPDeUq+vaHR3mbFziAzvf5GRn/EYH/B3D1Ht5BzLG2s6VgWR8cKTJa93rXrV2UEccLZmTrLrZjPHB/l7Ex114XYXhdzOUfy+qVFBIKPHazcFQNJsxv+YLD2fRgtekyVvFXxmf0/X3aSrmGaTQaDwWAwfPgwjYhHmM1+uRs0thGnGbdqAUfHi/i2JIxTlBJYXZ+CTXb2q+1EG0NauvkgOgXjltv526BrcjhS8Dg6lmexFZOkCktGJFknMjJOe7v4d5OSbxHEGXnXJg3iVSMTcQa2UL3GSNRJiej3eJCdL95eqWC04DBScJmvh7TjFNuS5CxJM0qQEkRnxEVw28RwUPMlg1UNo/5/D6JrHNmOMy7ONzgw5HNutrHq+W0Bh0fyDOUd5uohlbzLZEX7LfiO/ttzrF4B8sJTk/yzVz7g+nKLywsthFCkGeQcm5VWjEI3aB6fKGJbkmaUEaYp9U40ZTc6FugpIzzb5j96epqlZsRfX1mmHiTkXYtT06V15pYbFUC7jf/bTmLAVgVz/9d/eLPaiePUiRj9hdqx8SLfOHOF711YYjjv8OnjIzi29UAlRfRfjy+emuz933K3MLvhDwb970O/B1H/z5dPHBnacbqGaTYZDAaDwfDhwzQiHmE2++Xud79zed3YxmjBZampi4nxko9jSVZa2jzxyEie6ystgg1SKLsmhGmfM2SUrldQ7IZUKb7z4wXaSUraGY1wLT0WcS8MKQEODOVYaITUw7TXhOiOioBuEnR9NOB2M0AAORvWpngqFCVfGzY+MVXiuxcW2V/x8V2b68stFhsR0lJEyW2lgy1uv968K0nSjE4yJBm62dHvb7mZasQWeizkd751EduSTJQ85hohWedkBc9mpOBSCxKGcjZjRY+iZ/GjW3WttlC6UdG/eymFoNbWTRQpJJ4tcCxJ1mlkDeVtir5DM0yYb4SkmeIvLyxyYrLIsbE8f31pGYX2V+ia0n3x1DivnZ3nbz69jzBJeP3iMh/calDOOfiOvWUBtNv4v+02MrYqmLejzjg1XWG85PP5JybW+S1st3Fyt+g2el59fxbHEjy5r4wUzo4bO48yO1UDPCznWstGP1/utOlnmk0Gg8FgMHy4MI2IR5ytjOb6C53pik+trXe1jo3nef1iSNFz+MTRYXzH5spikz//YH7b595oKsPqeD7sVL9wqxYB2reg+9RRqm/iezGeUfEk00N5HNtiWil+OFMj60SEds+90RoU65sQ0GlQOBYLjZBqO8brxFwCDOddlpoRqQLHlhRcyWIzofs043mbyaE8VxabxOFtNw7Xun2FHMsiTlOCAZ0azxYopUdKbtUCCq5F3rXZV/G5udImzXTc5JtXlhnK2fx3f/spQJtb1jqRooeGcxwdL/YKoJdePcfBkTwz1YDxkstMNeysQ5J3LdpxxkjeoxHE3KgGoBTDeYeTU0U+mG1wYDjHTxwbQaC9LCZKzoDixuH5xwTv3ajxzvUqP3t6assCaLeGhxs1MrrJHjspBLdTqO2mcXK3itN+FUSmMlCSt66u8OyhIcZLvkmyYHvKmYfxXBsx6OfLoCa3uTcMBoPBYDCsxcR3fkgZFIcmpeQrX3isM6YBT0wV8V2LMxeXeH+mxt/5+H5y9k5DKdeT7nJiw1pz16YDPrfX+LakkHMpuBZP7isTpgrPlmRAuAtHzjiDm9WA0YLLiYkirm0xU21zeaHJrVqAYwmSRCEFZEpwZCzPE5MlhnM2rURxdanVG/3othqSTHspeLZEKZ1o4Q64PmGiUEr7MthS4NqSNFPMrLR1OkXnOEtAM85488oSv//mDcI4ZSjnMF70yLn2qkK3G/FX9G0sKdk35OPZeswk51rsH/IZLrjM1kM8SzBZ9pko5yh4Np4tubkSMF7y+fLzh1fFca6NDhwr+nz2xDhP7qtseAzcLoB2G/83KK7zykKT68vtbcfjdtlOHOWg822ncbLTyN6d0N9AqeRcEALPlpyfb257fY86W8VQPqzn2gl3eu8aDAaDwWD4cGEaER9SLs43+PFsnX/z9nX+r7+6yly1za9/9ii/8PR+vvqlE/zaTx3Bc2w+cWSEX3xmH6eny7x2dp6ffXLijs8p1vx9x88jRC82EzpjIXfRHiLnCKSAejvmW+fmePd6lWNjeYbyLo7Uq1j7jbQTdUYQp4wWXI6OFzk0kqMVZaSZIssyXMtiqOAyXfYpeDZRklFth6RK0YxSakFCLUhXnS/NdOOkHWcIoZUPllx91XvXTimEEEyXPdJMx7FGfdfS6oyBZGnGv3z9GlcXWwC9QvrqYotvnLnSO75bhBwfLxAmGVIIxoouEyWPgmsTJhlBnFLybSo5h4VGxLWlJt/84axOCFHZwOLZswTfPjfP//f+Lf7y4iILjWBdcbNZAdSVkVdyDjPVgErO2dHO8aBGxrnZBicnizsuBLdTqN1p4+RuFqf9DZTjE/r9VUpR76xtJ42dR5XtNJkexnPthN02/QwGg8FgMHw4MKMZH0L++N0b/NM/+YCCZ3NioshCM+I7FxZZbEZ86rExXnhqckP5eCWX5289A//u3dne6MVWYxFFT5saZnsVuakUVp8vw14iASk7Xg+dxbqWRdpJo4iTlEsLDZphwjMHKnz7x4sMO5IjYwU+uFWnupGJxiZESca3f7wAaOPIomdhWZJmmOG7khMTRa4stci7Om1hrp4QDDDm7H8ffMditOThSMG15TYZAleqVU0GgVaTVDrnm644XFlsrr4eQqBQpAqiRF9337F650Apzlxc7I0nuJZgthZycCTPRw9WODtTZ7Yekncsnj5QIe9ZvH+zzlw9QCrB9FCORpggUMzVIw4O59aNKpydqXKzGvQSNdpRwpkLSxwdK/CJZ/f1zu1ZgpvVgMOjhYHO+7uZQR80D39wNMeh0cKq47ZTCG4nIeBOzft264WxGf3jLWNFn48fHuK9GzUQikrOMeaCbC+G8mE8104wxpMGg8FgMBi2g2lEfAj5+pmrFDy9I92KEm0sKHTBeqqzG90IY56YKvces9AI+PFsg9l6yN/+6H5++z+e5o/emeG7FxdpBcmmZpEFt3uuFAE0o4QoUXfclIgysNZ8bq/8IRS6wWH1CQiqgU4EKboWoYJWlHFpscVMtU3JdxjKu9TDhIMjeYrNkJlquKPRE8cSxGnGG1eW8R2LOFVMVTwODOVYacW8e71KI0wo+Q5S6ISQTA1+xZbQyRpBkjHh2szVAhxLEqcZWBJf6saHQis9wkRRDxJqQcJ40cFzLBqhVlhIoQ1CVV9aykorZrjg9c7XjlOWGlFvFKAeJGRKEScpSQafOznBQj3Asa1ewTRxMkcrSphvRORci6VW1DPY7J6nv3h+5b1ZDo8WmK74nJ9v0gh03KdrCV47O9+bka8H+j6OkpSZarLnBdDaRsZLr567o0JwO4Xanfo8bBXZu9VzbnbM2gaKY1kcGy8+MEkeDwL3MobyQY68NMaTBoPBYDAYtkKoDQqah4HnnntOvfHGG/d7GQ8dP/0//jlFV7LSTlhqRkghyDmSOIP/7CePUG3HvD9T4/R0uSOfD3jzygqgvRhsKVluxXzm+Cg/eXyUr5+5yl9dWtrwfAIoeVI3K5QeG3BsyXIzWrVD/6BjD1BheBLGSh6+azNacMk7kjOXlknSbFW052Y4UiCFQiEo+Q6eLRBC0AgToiTDloIoyci5No0wIdnIBRSt6LAtwXBej0NcWmhS8CyKnsX1lYBMKWwpiRO9PksKVKfToIQegUgzten7UnAlk2WPku9yZbHFRMnjb3xkuvf1biH81S+dAODFl99huuIj+3JMv/nDGYIoZaKS4+J8AylgvOSRKfjZ01OrnmPQ4zOleO3sLD9xdHRV0b323Ltls8K83yywvxDcbWF+dqbKP3vlA5aaEVGS4dqSkYLLb7xwcsvn3WhN3dSRzda6nddzP1MaHhY+LKkZ95sP82s3GAwGg+FhQQjxplLquUFfM4qIDyFlz+by4m2pf5xmtKKUnCNZaASMFDzKnVhBgB/PNgDtZRDGCWGiaEUp/89bN/ju+QU+f2qcN68sbejToIB6mCGBcs5mrOSz0AjJuTZxkNz1tIvdILlt2jhoFCTMYKYacmpaR5/WpODx8QJJBq0o4dpye9PX1/WWiFJwbdg35HNloYlrWyRpRqYUYaJwLcFEyaMR6vdko1jUbmOh2o5YbIZYQgAWnuNwfMJBABfmGjqJQ4JrCxIFcaIQQNF3ePrAEGfOz9PuyFy653CENhpNMsXVpTb7KiCFwrEFr74/S9G3OT5eYLTorTNe7DYIFhoB5+eazNZCXEtyfLzA8fECb11dIc3o3Xf9O7vdx8dpyvm5JrUgxrUkYcdrop/djiH0Fzf9ox6DUgnulgT9G2eucHWxRdG3KfnaV6PrxfFPfunpTR+7m0jF7Rxjdrq35l5eow/r+/EgJIYYDAaDwWDYHaYR8SHkwHCOS4tNgjgl7hSbQoAt4HvnF3EsiWUJPnbQI05SZushkyWPKElphClRmnVSHGC+GfEvX79KwZHUwsG+BUKA7+hd+AxohjH1IGaAzcEDhxAg1OajHxnQilKef2wU17Z4f6bGVMnjwkITu6pVCN3xBsHqxJAMkEohJYwVXBabEUmmUElKnCpcWyCFxLP1eMXaBkR/o8SWAlsqwlSv2ZHapHKhGeE7Fs8/Norv2MxUA6I0wnUsskyRZjpO1ZJgW5JfenYfb11ZJkjinheHQjchbEtQ9h3CJKMVJ5RzbqeBYBHGKW9dXeHERJGj48Xea+xKyJebIT+6VUd0TBTjJOP1i0t8mtuLuQAAIABJREFU4ugwJyaKnJttUMk76/wGXnhqkq998xyXFpoUPQtXChqdEZCri02OjN0+125m5NcWN98+N08jSDpqDGfbhflud2rfvrZC0bPWeXG8fW1lW4+/00jFu+kvYTDsJdtpmhkMBoPBYHiwMY2IDyEF3+HTj43y3QuLWJbeQc/ZEiEFC42IJE0p+g7/7p0ZXFvyxGSRx6fK/NG7N0mVwpY6s8KWnZSFRP8iGMThOkm/Qhe5rc4Xqu2ERjvBdSS2pWjHD7IeQq+/4FrEaUa4yaxFK0o4P9/kJ46OUMnZ1MOEhXqI70iiVJFmiqKnDSizNaMVidKNg0aYUvIdcq6FQBCneozCkhLXlih0oyDJsl4jov9yjxZc0izDy/ToghAC3xZYMmW+EfK9C0s8OV3Cd2RnzWmvASEEKATNMOEP3rrJeMkjyTLCZHUihxSCTMGBYZ8bKwHPHKjwwWyDMMnwbEmYZJybbfD3f+ax3mO6u/S/+Yfvk2YwUnR49tAQQsB7N2q8c73Kz56e4u//zGMDi4hT0xUmyx4LjZA4VRR9myf3V2gECR/MNhgueL1RgmtLLaKyx4svv7PjJsDa4iZKtXHo+fkm4yVdoG9VmN/JTu3axkUYpzje6v+adRPrzvNmtmNs+KCaHxoMazFNM4PBYDAYHn5MI+JDyP6hHFXP5tBIQNGzaEUp8/WQWpjgkBGliijJyFRGrZ3wV5eXWW5FpJkiTZWW/6MoeDb1egrAbD3c9vlTdDqEdQd1lRSsMk/ca9YqDjKlDRm3ohHELNQDvn1unmo7QiCotWMdcdh5vpX27efpN2a0hX5dQZJyIJ9HtBUrrZgkzWhkGRMlj0wpfMdisuQxWwt6YxP9eDYstjLKnk2SKaTQjYuCFDSjlI8eqHBursFY0SPNMm7VIuD2yIlUioIr+cGNFcIkI0ozLCkRqX4NorNOWwrmahGOJTk0WqDo272RibJvU8k764ruU9MVDo3k+YmjI6u8Hj57wmOmGmzp6RClis+eGF/12NGiohWnVHJOb5QiUwrXthgt2juWa68tbsq+QztKaPQloWxVmO90p3ZQ40IpWGxGjAnRa+40wpTnj41s+Ro2YjvGht1jlhoht2oBS80YWwq+8oXHNnraBx7jI/BoYppmBoPBYDA8/MitDzE8anRz3h1LEMYplhQMF1ymyx4p2mcgSrOO54MgyRQXFpoUO54SCih6NkG0dYG+GXeyv6sU+LYu3u8GQrCuQZJ2ZiqGcxv37YJEsdSMaQQJBc+mEca0O02IQQ2XVW0EAUKInu9BtZ3g2RbjJRfXkjRCPaZR9m1+8vjYhsqM6yshR0YLVPKuPq/UJ26GKWmm+O6FBWZrAfuHfJIBhhe2FNpANMlwLEnesYiSrPefhCW10iLJMlpxyiePDFMPEsaKPo+NFyj5DkvNmGo74exMdd3z7x/KUV8Tb7rd4mGjxz65r8JXv3SCr/3yM4yVfA6PFqjkHGRn/KOSc3jlvdktn3/QOY5PFGiEKU6nwdH1rnjhqckNn+PGSntD34qzM1VeevUcL778Di+9eq5XJHfX2V3zR/ZXcC191WuB9gQ5Olbgy88f3tbrGERXlVLJOcxUAyo5Z12D5tR0hS+eGufcXIPFZsRIweHkVJHXzs4PfD8fdLpNnm6iS7cx9TC+FsNquj/Dqu1429+bBoPBYDAYHiyMIuJDSLco+caZK3zn/CLDeYePHarw/s06V5fa2FIX/Emmi1VLQJopqkFCwXNoxwntOCHYpcmDlCDSrdUN/SoF1xI4tgRSkmSTB90BngUISdJx3ZToxkTaGZ3YLAUjVTBecvnEkRH+8uIiS8141df66VdDgL7WBd/GloKVdsxU2acRJbSjlKGCyycODzNTCzk9XSZKUtaGZnT9OjIFh0dy/OBGrfP+KcI4pRnpYr8WJKAU3zm/uKrgFqBHP1RGosCzJbYlaXe9QADf0qtWCGwp+dzjI3zlC4/3dtDPzTZA6GZF3pF89f9+hwPDOZ7cV+ntQu8mbnA7j70TufZG5pTdeMqjYwUmy1q1sR0zyo12aj1LDBzZWBuTC3B4rEArTnlyX2VPd/K3MjY8O1Pl62euEqcZY0WP4xMFxop6rQ/j7L3xEdh7HhSFyd0yijUYDAaDwXDvuOuNCCGEBbwB3FBK/U0hxFHgXwEjwFvAryilIiGEB/yfwMeBReDvKqUu3+31fZhY+0vkrzx/mF95/nDvc0/tK3Nhrk4zyuiWyl2FQLf2Hc3bLLehHiakGRRdLR1f25PoFsebogSWVAPTKFYdhm4K5BxJ3rNYbsTsTouxnrGCzUhBpz2EnRo9654c7ePQCBIsCSpb7c0AWqHx3JFhxks+QZKRdVIp0uy2SWX3cni2IOiMVkgBjgXlnEOSZjTDlErOJudahEnGxw8PMVLQ4xjf+mCWxUbMWvqvc95z+K//xkn+129d5PJCE4ViX8VnKO+y0oo7iobOe9tZkwLSTCtgLCnIuzZJltGIUgS6keICaSYYKToM5Vx+4ZnpVd4PcaaLV8fSZovtKOXKYpNriy3eu1HlxZ87saviYTuP3alce+1YRD1IkEIQJSkzVf24v9tZ93bZqGGSc+TAovjGSpt6kKxbc1fpca/oXoulRsRI3iGIU968stK7/x7G2XvjI7C3PGhJFR/WxBCDwWAwGB4V7oUi4h8AZ4Hutt//ALyklPpXQojfAX4N+Oedv5eVUseFEH+vc9zfvQfr+1Cw2S+R3YLn7EyV719b4f2Zeq9oVkr/yTs6uWG+GXN0rABKcX6+gRAS3xFkYbqqObBlEwKItnNQ9/nQvhJxmu15EwJgsZlQC1LEJvqMDLAzBp7ftuCNy8v8/EdyeJZECDZJBRE4EuJMIRW4tsUzByo0wlTP5rdixooeT+0vM1b0+f7VJX481yRK0p5KZSNurbT4haf38wtP7+fsTJV/+K/fAQWeYzFWdFls6RjM/lcpuO274dmCgmdzYb4BKKQU5DrSENfWjamuXP/YeHGV98NSM+SP3p2hHaXYUt8Ds/WQZpTwe2eu8Fu/9PSuioetHrtTxcXAHfORPJWcc8dNgI0aJr/7ncuMFtePbPTH5O5UJbKXdK/FSNEljNNeYsf5uSanpq2Hcvbe+AjsLUZhYjAYDAaDYS+5q40IIcQB4BeA3wL+oRBCAJ8H/pPOIV8H/lt0I+JvdT4G+H3gfxFCCKXUgx2r8JCwnV8iX3lvlsfGi8zV2iw0k1U7+Uop6p3IxAvzDaTQLv5plmljy3vwGjxHkGw2H7ELFJCkasvXsdE0SJYJbqwEXJpvIOXGjRjf0gMOniM5VPKQUuBYFkfGir355m7DqOTbXJpv8Pa1KqNFl3obctIiUzHhALNKAfzgRo2zM9Ve0f6zp6d6xdir788yklNcC9arKrqXtRllLDVDPFsSxFqtUvJsJsoelZxDPUw4MlZcJdfvFnzn55qEcYYtBULo+FDPlkRJxpmLi7z06rk9kXRvJA/fqeJitzvmm61j7Tk3Koq7oyt3KjHfjlR+O8d0r8Xx8QJvXdUxoa4lWGiE96UxshfsZhTIsB6jMDEYDAaDwbCX3G1FxP8M/AZQ6vx7FFhRSnXruevA/s7H+4FrAEqpRAhR7Ry/0P+EQohfB34d4NChQ3d18Y8Sm/0S2S1U/t/v3yBOMkaKPpW84tpyiyRVpEqbMXZDI6N0Y02C6Py5G42JJNGRlHcrM2M3a44yRRKlvH5psecJYAloJ6q32rwjcGwLW0qmyh6fODpKtR2vkzb3F9O36iGVnMN40SNMMtJUdWIcV18DW3auu1rdXOovxgSKhWaMb1uoRI9dxNntZ9IWGYL5eogCHEtS9m+nqgRxSpwp/vCdGwRx2ouTPDFZ4A/eusm52TpRkiE6yRrlnIUlBfUgIUiydaaBdyLp3koevhPFxW52zHcqU9+oKP7EkaE7nrnfzhq2u87utRgv+Tx7aIjz802WGhGjRe++Se93i/ER2FuMwsRgMBgMBsNectcaEUKIvwnMKaXeFEJ8rvvpAYeqbXzt9ieU+hfAvwB47rnnjFqiw1a7ntsx0ZsseXwwWyeIUwqeBX0xmdst0rueA3uN1TGNtIRi2JcsBztrG9y99sVtMmC2HuHb+nqHqSJtRECGFALHthkvOlTbCZeXWiAEh0dy/O53Lm+4o/7iy++AUoRJxkjeZaYaIPuybiwJrhRIKclUNnCHMowS/ujH81RbMQLF9FCOVpTqRIaOdCNnC319JcQpZBm6M4Eex4jTjJlqwHTFp9ZOSDOFJeHyQoN3r6/QDBNcS2JbOmUlVQqlIOrEgB7qjDzA7iTdeykP382O+U7XMago/sSRIV47O3/HM/fbVTltZ53912K06OHa1sAm2cOG8RHYO4zCxGAwGAwGw15yN+M7Pw38ohDiMtqc8vNohcSQEKLbADkA3Ox8fB04CND5egVYuovre2TYTkzdRnFnCnqFyvGJIraUtKOE68sBYaqLybuUlLkj0k5TJMmgFupIye3evAKdBLEZe/mNECaw1E4YzruUfIs0gzBRBHHCteWAZpAwWXSptmK+d2GJNy4v8q0P5vjaN8+tixbcP5RjquwTJnoERqCIk6z3nnT/jtMMKSSPTxR6O5RnZ6r8s1c+4IPZBvsrPkXfxrUlK+245wsBnShUIYhTRZwoujYUOVvSjFIsoZsLjqVjXQXa1HKk4HKrFrLUjAiTjC+enmBfxSfvWjid2NEkUxQ8i48fHl71uu5U0r1ZPOZO2U6k5V6u49T07ajRr37pBOdmm+uiO3cSN7qdNWx3nbu5FoYPB3dyjwyKrDUYDAaDwWCAu6iIUEr9I+AfAXQUES8qpf5TIcTLwN9BNyd+Ffi3nYf8YeffZzpf/zPjD7E9trPruR0TvfGSzxOTRc5cWur5Q8DdVxLslFTpxkHBs6iHm1tXWkLPuieZ2lQVsVevUXafS8F8PdSjLZm+lhKdThEouLLYxrYFOcci6vg9XFpo9kwdu+hdyBYFR/D+rSZJprCk5PGJHPONmOVmRKr0e356usRI0e95Tbzy3ixLzYiib+M7FnnXIhQCSwosS3BktMDFhQaZ0qMUXYNJIfQfx7bIITgwnOslbjSjlIJrMVJwybuWVlUo7XsxVvT59ONjvHOtyrXlFkGcMVxwyLs27Wj1+3RlocmtesiLL7+zI2+DvZaH3+mO+V6sY7cz99tZw07WadQDhq3YyT3yoKVsGAwGg8FgeLC4F6kZa/mvgH8lhPjvgbeB3+18/neBbwghzqOVEH/vPqztoWS7Bc1WJnrnbtV450a1Z7R4PxsQvq136DfyplRAyXe2bESkShttbjUyotDfDBuZUW6X7jkkECQp7VAnSBR9h0aY6NejOskbqaKtUtJMIQQ0w4Rvvj/Ll5+vrnqfwjjhvZkGWaYQQpEp+PF8i5xjMVn22T+Sp5KzOT1dWVXQ31hpEyVZb0d8uDPaESYpWawYL7hMlDxWWjGebZFkiijJkAJsS2tE9g15BHFG0bM5OVVkphb2UhWCOKXs66jHtaSpYijn8KljIzSDtGeAeGi0wJWFJm9fW+HZQ0MDC5SzM1V+78wV/uL8IsN5h1PTpd4xXzw1zmtn54H7Kw/fC5n6bpsZ21nDoyyn344Jp+H+YVI2DAaDwWAwbIZ4mEUHzz33nHrjjTfu9zLuOy+9eo5qOyZKUs7PN2kECY4leHJfmV95/vCmv6x3d62WGgFvX63SjJINi/97iWRrX4r9Qz43VwLUNo4vehatMF13jGcJDozkuLbUQilBvINI0Y2YKNpkShJlGRJB2bdIFdyqBj01R4ZWa6iOAmGs6KHQoxHPHh5msuwxVw+5vtwmyxQz1TZBnHZ8MiDtmEwWPIvPn5zAsuS6ncaXXj3Htz6YAyBTiqVmRK0dkyqFa1kcHs3z5L4yy82Id25UWaiF2LbgcyfGGS64nJ9rstAIGS16/Orzh3jt7DxpmnFutgFCN3iemCpRCxKkEBQ9ix/dqrPYiEgyxXjJxZKSjx8eot5OuFUPOTSS5+pSi+myx5GxYm+t3YK8WzhfnG+glDYnDZOMZw8N4dpW75gHoQDdbSHcv2Pc3yTYyY7xXqVmPGzsxbUz3F1efPkdpis+Utwe7suUYqYa8LVffuY+rsxgMBgMBsO9QgjxplLquYFfM42Ih5+uF8DVxRZFz0IBjTBlrOhS8GwOjxY2/WX97EyVX/v6GzSChEaQ3DXDyb1msuTRCGOa0dbGlSVPOy+uVVBo/wht0LjdBky3kTBodCVnC05MlZmrB0RxRjWISTPdMOhvhHRsGfQoBDBRcglTxVjBIVOCkm/j2ZJakHBzpU2SatNHlD63FNpQ0nclT+0f4vR0mUrO4atfOtFbS/e++PFsg0YYI4UgTvUKpBA9f5Dpis9U2ace6obCwZH8wPulW9D+8GaVWpCsUmEA/MbL73J9pU0rSih5FtNDeaQQ+I7FJ4+O9AqQzQqUrkrg9UuLlDwbIQRBnOI5Fj/R9xyPCo9ik+Be0G2+9qtJuv/u/x4w3D/Me2QwGAwGg2GzRsT9GM0w7DGnpivsq/gsNSOiNKPsO3zkQIX3btQIk4inDwwBq6Wx3b+7BRAKTk4W+f61FZJN6nrfFgTJ7dLbswRRqu5K48IWkGzwxBL9S22aqW64A/0tBoFWDmToYj9Os1Xr7qKAILn9nNvJ4uge0xvDEDCUc7ClIANuVdvM16NeQ0cKaIZpT7mB0J+TQjdApNCfHPItfMdGCqi1Y+phglKKtDMy0T1ft3copPZ2aATJhqM4v/HCSV58+V2aYdJZizZFrAUJAkUzTLhZbVMLEr7y+cc4Nl7cMO5ws/nwszNVqkHCweEcC42IKMm4uRIwXfGoBdmqkYPNRhK6Y0bdkQ/fsfBsSSNItjW2cC8K+708x0bX1DQoNme3/hqGu8+jPBZkMBgMBoNh95hGxCNCmCo+e2J81S5zmCwj1mRelHyb92eqXF1qkaYZt2oB37+6wnIrAhSeY5Fs4Lsg0N4BrspIMkW54z1gJRmteGdxmtshY+PmgEJHTdpSgJB89vEx3r22wvVq2Pu6QhftJVfqnfVkcz+JDHAk7PSljBddpio56kHCviGP711YWrXmTHUaD4AlBELqNJKCa9MMU3zX4vBYgePjBd6+tkIYp9SCBFsKlNCJH+0oRUr9ehRavWF30izCOOHb5+aJU8VLr57jxGSBc7PNXhE7XfH5/BMT/NWlJYI4Zb4R4lqCDMH+oRy+Y3Fqusy52Sa/8PT+Oyp4X3lvluG8biyMFLQXhQDmaiHTncZDtwDZrEB55b1Zqu2Y4xMF3ryifSVaUUKtnfBnP5rjM8dHOTtT3bB4v9vmeI/KOR529tq01LD3bGSQbO5hg8FgMBgMYBoRjwyDfjH3bGvdcbroS8jZFufmGni2ZKTgsNyU3KqGOLbcNFkiSlJyroNEJyjEqWKLZMw7Rqc5dFQEShfh3ZEIp2tmmSkmii4LzZiffmKCf/1X14i7ZpsKSp7kY4eG+Yvzi9s653aaEN3WjiO1YmO+HtEIE2zLYqEeMMhmIlP6eNsSpEpgSZBSUvTg+GSJ09NlSr6Na0lmVgImSh6uLZmpBj3/hSBOSdBKEaUgyRRppLi40MKRcGKyxDtXl/iDt67z7KEhDo0WqLZjri+3iZOUC/MNQJti+rYk73XHP+Jd7ybfWGlzarrE969V8WzJVNljoRHSCFOe2lfmy88fXqWs+OKpcb5+5iqztYDJss+vPn+o9/VuEf6xQxXevLzCjZWA/UM+zx0ZxrEtvvbNc0yWPaJUrVILbGSO940zVxgv+XuiLrgXBnz32+TvQVBjbLUGs9v+cGCSWAwGg8FgMGzEXSohDfeaF56apNqOqbZ1zGK1HePZkmor4o/fneHMhQUuzTeotmPKvs2tWoBnS9JMcWmhQSNMdbyl2rwSj1KoBzHL7YQoVTiWWKXC2GuSbvSlFAihmyEZECZa82BJmG9E/Hi2zpkLi/iuxBZ6ZGSs5FLOeTSjFKX25mZ3OioMS+qMy0xpZcbp6TLDOXvTFI80g8myT9Gzma74HJ8o8pkT4xyfKFLJOcxUA57aV8Z3JL4jybsWIwUHBYyWPPaP5Dk6mifnWkhL4FqiF7MqpCBMM35ws45tCW7Vwt4YxlTJ5e2rVVAgUCgUjSjFtyVhokd5drubrJUVNs8eGsJzLBSCqUqOv/XRffzWLz29zpPk99+8QRin5B2LME75/Tdv9JQOv/7Zo1RyDnEKQwWXLz4xwc9/ZB8TpRxxmnJpockPb9ZWqQXOzlS5sdLuJYR0CeKE75xfpNqO1x1/Jww6x16PBNyLc2xEV42xV9frbq2h/z6ZqQZUco5RjBgMBoPBYDA8RBhFxCPCWhmsZwkKns0zB4eYqQYsNiPm6yFPTJW4sdLmxnIbgSKIMzKl4yM9V5KkirJv0Y6SDdUB/Tv+Uaqw714fAtAqhdG8RS1Ibxsk0PFXQHtD1IOYRhD3DCelgqVmhC0lzVA3ZbIsYxu+lqvoN6ZU6GaC52gzyeVmrM0uHQspJZOVHLP1kPYGFy4DxkseP3l8lLGinm/vmjSuMm/7g3d572aNepgwUvD45NERHMsiTlLevlbFtWPyrt1rOGkPCcWlhRaW1M0G37mthmnFGZW8zVDO5cpSi7xjE8QJN6sBTiNiuuJzZbHJb7xwcmcXp4/uDnUl53BsLM+bV5a5vhxSD2L+mz94d5Ui4htnrmhjVd+m5NuEScbVxRbfOHOFf9JpWnSP7Rpbdjk/16ToWcSp6jVaQKsFBqmCzs7UGc7vnbrgXowE3M+xg/utxtjJGsxuu8FgMBgMBsPDi2lEPEL0/2L+0qvncDpxh0fGiiw0As5cWOJmNeDwSI7zsw3STOHYgizTCQw5RxfrmVJagZBtz4RyI0PJvWSxlaz6d68x0Pm3yvTHAl3wx0rhSEGSZSy379y/oj8do/vvKMlYqEc9H4g0zfjhzSq2lFgbNGUEWklxel9lywLzy88fXhdNeG2pRaa0AiXLQEnFSjsm6RiFajWLIk5huRUzVbn9nEvNmOlKjucfG2OhEfDOtWonHhNGCw6+Y61StWxXmr/2uC+eGue75xf58w/mCZOMqbKH71icubjErVrIiz93glPTFd6+tkLRs3rNEt+xQCnOXFzkpVfPrTrv2qK8FsS4UlDsUwx01QK/9lNH1sn1l1sxnz4+smrdu1EX3IuRgPs5dvAgmEA+CGswGAwGg8FgMNxdzGjGI8paeXf/TvJyK2Gq4oGAONFqCAE0wgQpIEm198ODHOHZvzYBuI4k70qkvP31KFObJoBsF7szjtH1wkjVagPNNIM0UzTDmGDACS0BjiUoeNa68ZlqO+7FX3YZJDufLHscHi3w5L4yUnabR7ffI4FOxJCd8ZU4yXrnsKXoFXZjRZ+CZ3NotMDTB4f4xY8e4HMnJzg4kueV92a3Lc0fdNxrZ+cBmCr7HB7JM1zwyLla9bDQCHtpLQKx7t5qxylLjWjdeU9MFlZdM9eSNMKU4+OF3mO7zZxB1+0zx0fx7NX91t2oC+7FSMD9HDvYP6RNV/u51yaQD8IaDAaDwWAwGAx3F6OIeETZbCd5ttYmzXRxHCc6wQGhRy7acfZANyAGoYAwyfBtsSeNh67aokvXByLuyC8sQW8EBHTDI2cJ0o6hpmcJUqWQCBKlsIRgrOhyYKSwbRf5tbLzF19+h9GijRQOnzwyzF9dXkYI7VppC1BCXwnHkhQcibQEM9WA/UM5vvKFx3jt7DzVdtxrCjhSrirmuzvO25XFb3Tc65cWQbGqCdY1xOzuaH/sYIUzF5cQQuB1fCrm6hGTZX/d852bba66Zk/tK3OzGtAIEs7PLbDU1I2Wr3zhsYHXrdswWW6GvRElx5J85fOPbXUbbMi9GAm4X2MHD4IJ5IOwBoPBYDAYDAbD3cU0Ih5R1v4y71qSRpCwbyjHhbkGAsg7FstJwkPXeRiEgla8+xciO+qQVHWUFrY241Qdbwq7o25or5lHCeIMKbR/hG9bLLcTbKmbBELqJkU3GeJOvQkuzTe4VQ9pBAlTFR+lVCetQ+gGhGdTdG1yrsXnTk6s8p04Nl7sFfOjRY+pksd46bb8vbvjvF1Z/EbHCQSOLXRjqDN6ESYZnm31drS//PxhbtVCFhohtSDGsy18R/Ls4aGB5117zf743Rv89p9dIE4zRgsu0xWf187Oc2y8uO7adhM6fvtPL5BkitGCy1R54+PvNQ9CQkU/D0Lk4oOwBoPBYDAYDAbD3cU0Ih4x+gubnCOJk5SZakLJs7i80OTacgsptDg+vhfmDneZnC2IM0V2h0oICZzeV2KlFRPEKdVAj6dYChxLIoVO7PBtSVCP9NhDuv66SaH/ZEp/XUBPneEKxeGRPMfGi+se1/9+uZZgpRlxaamFQPCxg5WeyeOJyQJ/8NZ1Cp5NybOohymuLTleLhAmiqJnoYBGmDJW9AaOe5yarnB2psrvnbnCX5xf5MpSi1PTJXzH7u04v/Le7LaMEjcyVPzYwQo3qwFXF1ugVG9NR8cKvTWdmq7w4s+d4PfOXOHta1WiNGO06NFakziykRz/3GyTTx0bXXXuajve0FDx3GyTTz22/ePvFV21RiXnrBpHud/pDw+CCeSDsAaDwWAwGAwGw93DNCIeIdYWNl1J8xNTBV6/uIhjQS3Q8Y2PArbURplF16IepT3jBqsb87mNl5mhC959w3lOT5eJkpSZasBfX14i71qgdDJIzrXJ2RHtZPDzpEr/cVA0k1THfNqSNFV4jsVU2e95JPQ3Hs7PNQiTjGorYqEZkWWK8ZJLO8r4t99v8t0Li/yXP3eCc7NNnj00xK2aVhFUcg4nJ4sUPRsFvH1tBYHg+WMjqxIq+um/Pz59fIT3b9b53oUlfur4KF88Nc4r783yw5tVri+3OTlZ5NAos6K4AAAgAElEQVRoYUNZ/Eby+V//7FFAJ2NstaZWnPHJoyOUfJuri03euroCwOGxjc8LOzczfJDMD/sbT1eXWkyVvPuaUGEwGAwGg8FgMNwPTCPiEWKjuf3//S8u0wqTXqxk16F0KxHBWq+EB400A4WikaVYQiCEIlNgW5KCa1Frx9tK9Liy1Na+Bp6NZwuWmhGWFDTDBNe28GyJLaGYcwn70jL66V6rONMfCymxhCDvW6QKbtUC2knK1aVWr1H0zfducXOlzf7hHFGqSDNFkmbcqoUM5VwKnkUjSPjtP73AwdEcT0yVOTJ2W1WRKcWPbtU4PV3hyX2VLaX9q+8Ph4mTWtUQJymvnZ2nknM4NV0m71h8cKtBK045PV1ZJYvvL6Tzjuw0bpJ18vl/8ktPb3rN196r3dc1UwtxHWtTOf524i3XFvxRnHK0T5FyP8wP1zYK3766TK0VU/Tt3pjM/UyHeNDGRAyPDubeMhgMBoPBsBbTiHiEGLTzG8Q6whB0OoYUemTAetC7DFsgud1IyQBXKqS0EAKSTNGKEjK1/WZKkGTUg5gfLrWYKHmMFhxuroQ0wwTlWiwthji2hS0hGtCJ6J6j6zGhlCLN9FiCJeHqUgu5Isg7NiNFl+PjBZZaEa4taYQJUaqfNFOQpQrbEkSJohHGhElGLYzJOxZHxorM1wPOzzeZWWnTilJytsXhscKW0v6NlAF/enaJTx4d6RX2R8eLjBT1Tn2/z8RGips7GSUYtJZDowUc2+Jrv/zMpo/dysxw7TrjJN222uJu0t98ma8HBHHKQhjyrXMxnzsxznjJ3/MGyU6iWB/EMRHDw4+5twwGg8FgMAzCxHc+QgyKvTs7U8e1xe2ivFMxD7A5WIcUt28Qsacr3R4CcK3158472hCx+/lMQSuBZpSSpClFz8ZzbBx7fUzkICwBI0WXMMlQSkdJVtspeddCoKi2E21eqTIQAlcOvhrd65t2xjlacUaUZCRZxkIjJE0zhvM2YawL4zjNsC3RMXPUfhRKgRAQpxm1IEEIQcm3kAjeurrCO1eXeOvKCrV2TDNMKPs25+YaLDbCXpHbHQFZy0axiAq1KuUCBu/M9xfSUogtz7cZu4lo3Crecu06j4wV9VhLPbzncZj9dCN15+sBb11doejZWAIaQcJbV1a4NN8YGOd6p2w3ihX29r01GPox95bBYDAYDIZBGEXEI8SgneLlVszHDlQ4c2lZx0tu8Rw5G45Plrk016AZZ1gdA8bdiCfuVHyhAJQuxA8O57hVDVhpx7RibQZpSz0K0X98O4GgETFRcmiF2zuPa0nKvkO1HSGF4spikySjk5ahmzGVnEO1HeM5EgFE0fpXNOg1ZkCcwEjBxrIkUap6aRICWGnFCAHKs8gy7d4hBTSCGKXAdWwKns1wQSddvHF1mZxjMVb0CJOU8aJHmGScn28yXvI3lfZvpCT42MEh6kGypUHlRoqK92eqvPTquR3Jrncb0biZmeFu1BZ7zaARkVv1EM+WvfugHibEWcatesg//sXTe9Yg2W4UKzxYPhqGRwtzbxkMBoPBYBiEUUQ8InQLnkYY8/5MjbMzNSo5h88cH2W06DNWcNYVylJA3pFYUjBd9hgvumQIfnSrTjPOtOHjHqxtN02MOFOUPUkrTmnHKXnXxncEni1XNSHWnm+2HpNuw61SChjOOxyfKJBminqYopTQox+ZIlVgScFwXptC7iSdQ9AZgen8y7clYZIRxClxmhKlCktAwbXJlMCSkrJv4doWSQZl32ai5GJJyfHxAofHCjiW5Oc/Ms2njo0yUfJ7aopGR12wmaqgqySIk5Q/PTvH65cWCaOElWbEn/1ojm99MMdsrU21HQ/cmR+kYri62OTaYntbu+6D1rKRqmEzznYaHy++/A4vvXpu3bl2o7bYS9YqEqbLHm9fW+HmsjYqDeIUIQQ/c3Kcn//INIdG8nuq0ugqMPrZqAB8UK6Z4dHD3FsGg8FgMBgGsakiQgjx7GZfV0q9tbfLMdwJ/TO4T0yVe7vLLzw1ycX5Bv/0Tz5gpOgz34gBXahLbqdLpJmiHsTEmS60+3M1tpM8cbdwpMCSuqmQ9xTjJQ/XllxdbGFZWz/ekuBLPbaxEb4t+eknxqm3E2aqAc0wRaGvjWMJolQRZ4pbtZCiZxEmqhffKbit9ui/TJZYPfqigOVWzFQlxzMHK5yfa3J1qUXJs/n44SEWmzG1IMa1JE/tK/Pl5w/zm3/4PkuNiOGC9pMYL+kif7Ls99QLxycKvHllhTDJKPt2r4Gwlaqgm1YRJgmvX1xGAKeni8xUQ/78g3nGix7TQ/4q6fRGiRofzDY4MVm8o+SHnUQ0dhtt789UubbY5sRkcUNfjN2qLXbCZh4MGxly/vWVZZZaMWNFj6f2lxkr6vd2rwuz7Zh6drmX18zw4cLcWwaDwWAwGAax1WjG/9T52weeA95B115PA68DP3X3lmbYLoMk2MvNkN/8w/dpBAl518KWAt/RAhgpoB1npBmkWYYAGoMcGO8jFd8mVYr/n713j9HrvO/8Ps9zru99rhwOhzcNJcqUGEmOLzIdR7nIRhQ4yKJps150HRhtUSdp6hTCuqjR/SObRTcIkqBC1912kW6AGvFusvEiab31Rl7ZseM4K0uRZEumRHNEkRLJ4XDu817P/Xn6x3nflzPDmeEMObyM9HwAiTPvnPec5z1nCPD3e76/79ezJUpDrWBTcGwqvs1yJyaIsxueI1UwUHQgTK5rRjgSbClwbcl8M+atuRaZ0jiWIM1yJUS2qpsQpZrRso3v5P4POkxy008JUgq00n0Ty967ehGimrwx0QwThkoeJ8YtZuohHz02xFi1wAPd45XWzNRDTozX+O1ffKjfXKqsajJ85tRhvnFmHoChkseDY2XOzraodp//ZkkTPXq/K0mW8ddTCwRxhmMJLi+HPHKwRitMsaRguOTy7bNz/NlLl/BswSMTAxsmahwcLHBkpLTmGrstu17daLuyHDDfiphpBBxaaPPIwVp/3rz3uXtqi9UNghvdl1td13jN58J8i6f/9CqHhgs8NF7j9St1ToxX17zn8HCJTpJR9pzrnu1uF2Y7KQDv1D0zvPcwv1sGg8FgMBg2YstGhNb6ZwCEEH8KfFZr/cPu9yeBz9/+5Rm2w/oZ3IVWyI+uNskU2Fa+6x9lmg8cHuD7l1boxOqeD8xQWnN0uIQl8zGMVpQXzFGq2F8tcGGhdUPvCQ104gwpr2Vs9EZRVDfVwrYkDx+oMd+M6MQZrShBCgjTtaoHKUAjSFKFRiOlxBUKIUR/xGK4aJNpWAnSNWuQgGMLVoKEmXrIxECBj90/jGuvlXWs3q3e6h/vk6Pl/utHR8r82k8f2/Y/6t+YqXNlOeDSckAnSkHkMaiLrZh6J2ao5FIPYn5wqY5nS7JM0Uphaq5F2bevS9R45rmpbe+63yy95kmcZlxaCfBtiSUkc82QVy5qHjtUoxWt7TTtRG1xq+vqpWBMzbVAQL2TNxYuLwcUHeu62NCHxms8dXLsthdmOy0A78Q9M7w3Mb9bBoPBYDAY1rNds8r39ZoQAFrr00KIx27Tmgw7ZL0E+9xcGyEEQ2UHAYRJhmcLYgXjtQLn5tt3d8HboB1lxKkizhSOJciUphNpUgVCaMLNDCLWkSpIu9GYAojS/Gut8xc8WzK9ElAPYpTWRGmuFFlNpeBQ823qQUymBZMjRZY7MVfqIb4lODrgs9BMiDLNoxNVnr+wjNJ588K3JWXfRqk8DqNnltjbTYfNd6t7/3j/2mvTfOn5i/w/P5hmrOrzmVOH+7GavdGAP/ru29eNBqwfGzg+VuLSYsBCKwI0aXemxMr/YLoeIrpjJbVCbqaYaY0gbwZtZIh5J2TXvUbbCxcaFGwJCCxLEGe5P8aZmSY//eC+XbveTtcFcG6+jWdLPFvSjPLRmQfHypy92mKo7F13b+5UYWYKwJtnu9GnBoPBYDAYDIads91GxBkhxL8Cvkxes3waOHPbVmXYEeuLwYVWhNM1OBQCXn5nBdcSNIOEZpjc5dVuDwVcqXco2hblgsvjk0N0oowXLywy24zRGgquZLjoMtuMiNflkcpu2kev8dBzZe0dJgBbCBwp6EQJK518JKHs2WsUDa4tODRYYKkdEySKwaKL0nnahQTiVLHUTjg8XCRJFUtBStV3iJIUKSVKa9pRhkCzr3pNtbLRbvWHjg5c11ToeXyUPJt9ZZdGkPC7f3kWgMnR8prRgLcXWjz9b69ycLDAvorHbCPi0FCxbyL5xb96i7Gqy0wjpBNnfUWJAnxLEGWa2WbExEABz87vmC3zP3uGmAutkNPTDeJM8cxzUzx1cuy2y657jbZWmDJa8ZipR6QqH9vRWrMSpLsWeXkz66oVHFph2vUQUVT9vCHYG8OoFRwjSd9jrB+72ciLxGAwGAwGg8Fw82y3EfFfAb8O/A/d778D/J+3ZUWGHbO+qB0u51GPo5W88P3AkQFOTzdAaNrb8Fa4FxDkRX7JdTh1bIiRss88IUXXwZYJwyUHpQUL7RjfsYizvHlgy1wFsdpks+xKwlTlkZyrLlDrJlT87VuLKKWJU02q9BqzyUxpZuodwiR/oeRKZuohSaZxLUmm82s9eqjGUMljph5y33CRb52dR+ncIKI3CPO+/ZU1n3H1bvVmhc+bs01Knr3K/yP/BF96/iKnJof7owELrZCzsy0AGkHCfDOiGabsr3lI4XR9IRRBojg4WODMTILVvRm5QanAtTRCCCq+Q5RkIARF10IDjTDFlvD8W0sI4EP3Da4pznoKje2yk93mXqPN6bqrjpRdFlpxHqUqBD95//BdKQ5XNwDLnkWjmwxwciL3heiNYez03hjuPjuJPjUYDAaDwWAw7JxtNSK01qEQ4l8C/0FrffY2r8lwE2xU1NaDhIpv41gWk6NlPn5ilM9/5bW7vNLtUfUt2lFG0bMYKV+Tv0uhUVqz2E5yDwOlEaj+7n66aqxCkJtJVgsObqJYCRKkENgSHEtSLrj4jiTNFK4lc+WCANE9mS3A7fpTaAWDJYeVICVKM1KliFX3WOBbZ+f50JFBjo6UmW8KhkseSZaRKo0tBY5lMVB0Ny3ANyt8Li13OL7vmscAQMWzmG2EvHBhkXcW23TijExphksuZc/m/EKbJNN4tuDVS3WePJHfv+GSy1I74cP3DXJ+voXSGksIhA2+Y1P1bYqezUTN52/PLyEFHKj5jFY8rjYi2lFGteDw8IFqv8kFOy/OdrrbfGK8xsdPjPJ/zDZ5e6FNybP48NEBxmpF6kHCp08d2fa1d5PVDcBqwaERpjw4Vmao5N02A0rDnWG97w7svgmrwWAwGAwGw3uZbTUihBC/CPw+4AL3df0h/qnW+hdv5+IMN8fqAumFC4tcXOoQxinfmZonu5t5nNtACroJHzZxpulEGQutkHNzbV6fXiHtKhFSpYkzRZzpNWMZqw0sBbkXRBhn+K6Nl2Q4lqTgSBphShBnKKVRGiwpcW2JFBqNwJKaJNUEscKSgkNDPvONqJ8u0r+OBksp5hsh3zo7z28cqBBnmpMTFX443SBMU3zH4uREhfMLbZ7+t6+SZIrhkkuSZvzhdzp89on7Ni18HEvSjLK+EgKgGWU4UvDGlQZSCEquxWI7Zno5wHMkJc+m6Eo6Ucabs01aUcJYtUDRtWgEKa5t8djBGi+9s0KGYqCb3pAqzS8+up8fXW3zUw+McLURstROuFqP+NyTx/jW2QXGaz6y133h5oqzne42n5mp840z83zo6BA/NlHlzEyT12daDJW8XZfK79QXYH0D0CQDvDvYSfSpwWAwGAwGg2HnbHc047eADwPfBtBa/0AIcfT2LMmwG5wYr3F+vsWfv3I5n1nXmk6sSDK9adrEjVIo7gRKQ5ppGkGMZQmWg5ivvXaV0XJeEPQaKQJNnF7//tVNCNeWKK0IUoWUCkcKgjgFbByZR5debcQMFx3ibhRnqiBJFap7DoBUaS4sBNjimvfE6vuUKCh5FkNFh794ZYbJ0SJTs21Gyh4TAwWiVHF6ukEzShmvFRguuUSp4uxsiwfH8gSMzQqfDx8Z5IdXGkCuhGhGGe0oxbMEQ0WHZpSrIRwpSFJNO1bYlkIC7Tj3vYhTRT1IuLKS8umPHKITa0q+w6Gh3PsiUVD2bH7h0f1880cLLLVihsou9+8rM1rJFQtTs+1dK852utu8unFRKziMVa+tY7ebELfiC2CMId893AkTVoPBYDAYDIb3MvLGhwCQaq3rt3UlhlvizEydZ56b4vNfeZVnnpvizEydLz1/se8vEGeaoivxHblls0GK7f9S7ITNzik2eE0Bcaap+g5Vz8aWgrlWkidPsPEIxvrzSAFxpkiyvHkRpYokUxRdGylyY8Z6kJAqzXw7JlMKC8gyTe/U3VCJPqmGkmtRcCSW6I5+dEc5jg6X2Ff1SZXmR1ebLHViLi51uLTcIYgzWl31RdW3EULgOxaenftNTK8EPHVyrC/nV1r3v/7cxx/gCz//INWCw1wrplpw+MLPP0iiYbTiMV7zsa3cK6G37pJrEaYKNBQci3asqBYc3n9ogE6seerkGBXf4Ynj+/iVU0f5z94/wUjF48ULK1xZ7tCJE96cbfL112eZutroNwk2W+NOjSInBgo0w7VdpK0aGtMrARV/bc/0dsjkVzc8pBD9r589Pbur1zHc+/RUZbWCw0w9pFZwjFGlwWAwGAwGwy6yXUXEaSHEfwlYQogHgN8E/tPtW5ZhJ2y2k3txsc2hwby48+zcC8GzJcGq6EtL5AaPUdYtvG9SEiGBrQI1N2o47Cs7rAQJq/0zpcjXkGnoxBmDJZfRskeUKs7Pt3BtQSdJ1zQJNlqy0vnnEhKkEJQ9SZDkKokoycgyfc2QsnstxxKM13wurYT9NVsib0D01CJJppBC9FURWoNj5+MQYZLhOYK3FyIO1DzasSJIMuI0pmALEpGnKviOBeTPZLEdc+rYyIYpGqtjHj/5yMSaz/el5y/SCBJsmd/ZVOfPQEB/VKVasLEtydGRMqcmh1FaM70S8OzpWbJM8cZMI0978G2W2xEAQZL7bRQcSZxqXnx7GceS3Dda3nKNO2Gnu813SiZvfAEMqzEKF4PBYDAYDIbbx3YbEZ8D/jEQAf8G+Drwv9yuRRl2xmYz91KK3E/AUrSjhEaYsd4iItOQ3UKQhiXAsfLCfH2E5prrbPDaXOv6KNHV68uUZl/Fzwt8O1dy2JZEJALXgjDdumsipWC8VuCjx4Z56e1lljptLJknXfQ6Cz27A0sKPNuiFWf4tiDJNFKC1nlnpHelMNV4Nni2IExz9UTBkYRJRpQqMqUoeRZFz2GonDccwiTjaj1ksGQTpYpOnNKOMtpxii0lx8dKwM4Kn8+cOsw//fdvECQqN9xM8zhO15EMFV1cSxCliijV3D+an79XvL9+pc7lpdxPouxZREnGbCMCNOO1AgutmEzl0aWtKGNqtsWv/8yxHa9xM3ba0LhTMnnjC2AwGAwGg8FgMNwZtpua0QH+sRDid7TW7du8JsMO2Wwn9/BQkbcX2tQ7uZR+t30qJVB0JbZl0YmvbyrslDUjFiL3Zrh/X4mX31khShVV38G1BJ04Jc30liqMXL2g8xhKYKkTYwlohQlxptd4SUyOlOjEGa0oT9UYLDpcbcSgwLaujYFY3etF3QZI7/tWmDLfjDi+r8T5xYAPHB7gwmK+i+7ZEq01UgoODhZRSnH6SpMgTlEaCkWLL37zLYDrVA9b8clHJviTFy/yysUVljt51Oa+qkPZd2jFuUFmJ1bsq7gMl9emODx/fpEozWhECXGqcG2J6H6uWsHBtSVL7ZhOnFHyLA4N54X4M89NbdvEcTPWGzr+Nx87esPz7JYS40bsRV+AnZprvtuubzAYDAaDwWDYmwi9DS2+EOKjwL8Cylrrw0KIR4Ff1Vr/d7d7gVvxwQ9+UL/00kt3cwn3BM88N8WF+RZXm1Ffar+/4nHfaJmpqw2+8+Y8Qay2HJ24FUqupBOrTb0nbjS2sRECcKTglz4wQRBnnJ1tMVCwWemklFzBa9NNhMibDZtd1xJQdCyOjJS4uNQhiFO0zscYVvNjE1WkEIRxRpRmWFLSiRJmmzGqu/7Bgk2jawwpJQghkEIg0Ti2ZF+lgC0Fk6NF9lULJFnGubk2jTDBtSQnD1T59Kkj/NZX3+DKcocgUYyUXWrd2EeAZz716LaLuDMzdX71j19hoGDjOxb1IGGmHjJW8bAtyeOTw1xa6jBW9YgzvaZI/Ad/+DyvT9dxbQvPFkSpzu8NgiPDRaq+3VVTKB4cK1PybIJEUeuma/QK9J3OzK8eIbqV89xO9lJhfbfv592+vsFgMBgMBoPh3kYI8bLW+oMb/Wy7oxnPAD8HfBVAa/2qEOKJXVqf4RY5Plbiz1+5TMmzqXgWjSBhZiXg506OMb0ScHioxKXldt6M0LufjNFeH2m5jptpgPi2oOjafHtqnuGSx8HBAg8fqHF8rMSXnr+I70gyrVEqb0RsNBViS0GmFdMrAWmm6Po3Xsfb8y1GqwWU1jxycICyb3Nuvo20OszUQ6QAz7MponGkBWhWggQhcqdKz5Z88pFx6kFCnGa5maNSaK2JU0UYZyy1Y/7ou2/TClMGig7DUva9Iqq+zWI7vi6+cqui+NnTswwW8xECIQQDRReA5U7McNmjVnD41M8d37AgVBpGKh5xt9ngO5JqwcfqzqkstmOGSy5Hh4tImasldhK3uRk7je28G+x09ORmGxe70fC42/fzbl/fYDAYDAaDwbB32XZAgtb60rqXbsFZwLCbTM22+fHDA9QKDq04o1ZwODZS5F9++zzfPjvHhYU2nVhhy9wM8naxWw0OS8D+WoHHJ4coOnlqxonxKvUg4d+9PM18M2Sk4lFwbGxL9A0b15/DsSWp0iitKbr2putrJ4r37S9z/74yR0ZKjFZ87h8tUfEdRsouvi0Jo5RGkLHYjllsJ2QKhNCkmaIepHz1B9O8caXOXDPi4ydGee1ynTfnWiy0IuabET+cbmBLSLKMs1dbvLPY5vJyh3aUEqWK4ZK7xhSxt9tcD5I1BqRnZvLwmumVgBPjFaJUESYZWmt8W+I7Nv/r33+Upz+xcRMC8saHZ1mMlD0mR0qMlD08y+LoSIlnPvUov/DIAQ4Plzg6UuazT9xHlOldSa24U+kXd4obPaPdft967vb9vNvXNxgMBoPBYDDsXbariLjUHc/QQgiXPDXjzO1blmEnvH6lTiNIaEUZUggWWxFXGyFRohiveYQxhCnEWW4sma2TD2ymZLjTWAKs7tjDR+8f5txcm7Jn5caRQhCnGRcX8+J9f9XHtST1IF4T5dlD6dzs0rMtDg0WidKM5U68RjkhAM8SFDybB8aqAH2zwnPzbTxbUnAsllpxdw3XlBcCSDONEAJL5saQjTClEaZ87dUZhBAcHioy34yIUkUrSnnp7SVSlRtkhkmGa0surwQM+DaPHhpYY4p4o91m1xK8fqVBlGQ0wgTfllR8h4/dP9xvQGy26/7wgRpFx1ozynNkqNhPxljfwNgtE8d3mxnkzSoCdktJcLfv592+vsFgMBgMBoNh77JdRcSvAb8BTADTwGPd7w13gTMzdZ55borPf+VV/uc/f41zcy0aYYogj2e8tNwhTjJsS9COFftrPvlAwbVki54xpAQcK4+6dCyBFBtHbd4s2z3XQMGm6jv5uAPwrbPzvH6lztVG2FdxnJvPGxMlzybONL5j4doSa5M1Z5nGsQRhkjLXjK4z69TkhpjDJYfplYCnTo71TR2bQYLWmlaUUfK6ygsrv4pnCTxbkCr6iouldkwYpzw4VubFd5Ypexa+YxFnCikgTjPOL+Q+FfsqHorc38K1BJ5jIaXkqZNj/bVttdt8ZqbObCOiGaZUfJvRkotAMFRy+ZVTR4Ctd92fOjmGZUkeGq/y5Il9PDRexbLWXn81q++L0rr/9WbHb8b687y90OJ75xd5/Ur++7xTRcDd5mYVAbulJNit53Kz3O3rGwwGg8FgMBj2LttqRGitF7TW/1BrPaa1HtVaf1prvXi7F2e4nvUF5utXGqRZLs9faMW43WI5VVD2bGwJc41ojU+DFHkR3vtP6dzUseTZlFwLa5c6ETtRWgigGSYkqSbLNAvNkE6U0QxTFlsh3zwzy9mrTWYaISXX5gNHBvAdiyxTZDpXUljrznl8f4VHDg4wvRIiENgb/LYXXAshBBMDhX46Q63ggMiVGbWCjWNLagWHgYJL0ZFYUpB2Gzquld/MnpKj4Fokmeo3RqSARpii0Pn90NCKMg4PFjk+VmGo5GJJeZ3B38RAgWbXxLJHb7f52dOzHBoqcurYEAXXJlaasm9zoOav8ZDo7bpLIfpf93bde59zph5SKzhbGgzu9PjNWH2eH11tcPZqi+P7yv2xm5sZT7ibbPWMbsf71rNbz+VmudvXNxgMBoPBYDDsXbY1miGEmAT+N+Aj5LXl88DTWuvzt3Fthg1YL+tOMs1Qye0WuClaaxxLorVCCoHSikZ0zc7DEuA5kqibcjFUdmiFGVGmmag4lF2L1640d2WtOxn3qIdp/w0K0AoQECaKhkjJdIhnCYJY0Y7y9IuPTA7zw+kVHK3xHUknznCEQJOrJZ48Mcbzby0ghEBrlfs6rFtXpjSzjYjjYyXgmllhL8rx/HyLdpTSjjLiTGFLQZzlyRmuJSk4+V+h8ZqPJQVnZpqMlj3eWQr6IxhZprCkwJLQjFIypakHguNjZcIkP++zp2f714etoyT/6LtvM17zkcJhpJzHtiqtmamH/c+1WaRrb9d9p6aMOz3+Rud55rkpJgaKe9ro8GbjPnczJnS3nsvNcrevbzAYDAaDwWDYm2x3NOPfAH8GjAMHgK8Af3K7FmXYnOmVgChN+d75Rf7jG1epBzFhkpFqzeRomYODRQ7UCu8SmbkAACAASURBVBRdi06cUu8ka96vgShRfTXExECR+/eVGSg4zDdjfrhLTYid0P8lFN0mhM6VBr6T/yRTmihVVHwbS8JSJ+bbZ+e5MN9Cayg4kqJrU3CtNeaV882Qqdn8mCjNFQlS0Fd89EY+9ld9vnFmfs1ufG+39+EDVZJM0UkyXEviOxJb5l4Rxa7EYrjkUnQttNbMNSOGSi6OLYiSjDDJY1MzpbGEQGmNZ0tWOjFff32W8/MtDg8WrlMErN5tPjPT4I2ZBq0o4dnTs7iWuOGO+m7tut8u3g1GhyfGa3z8xChvzDT46qtXeGOmwcdPjN6wMDdKAoPBYDAYDAbDe53tmlUKrfUfr/r+y0KI//52LMiwNZ4leP78EhXfpuLZpJniSj1kolZgcqTI311YRgOnJod44cIynXhtuInS1/whLAmXl9osddK7ZlZpk8ev6FUL0ECSajxX5gaRKvd6uFIPidN8FKMZpLz8zhLjNT9vNHTVCvmxEs+WvHB+CYDRssNMI8SyBFprlM6bEVXfJtPwofsGcSzrut34E+M1fueXHkEAL769xHInQQOTo7mSoejanJyocm6uTSNMcC3JcMllvOYz34yQCDT5/DzAoaESmVJMr4Qo6KorMr53YZmDgyHHRkpr1tD78+JSh4ODRSq+TT1ImG1EKK05MlzadEd9N3fdbwfvBqPDMzN1vnFmnofGqzx+3xDNMOUbZ+aZ7Jp+boVREhgMBoPBYDAY3ststxHxLSHEF4A/Ja8TPwV8TQgxBKC1XrpN6zOsQ7PWmLHo2gz4TjeqEh6fHEIAUaYpOLkqIkv1defQQMmWrAR3pwnh2wIp81ELDTgSklVGFilgK40Q+evL7YRM5+oJKXJjzXqQcn/VY7GVUPUdJmo+08sBC62YVGmkTDg5UWGhlVDxMjpJilKiG3UpcG2LfRWPkbKP0nrT3fgo0zx1chwprt35uWbA355bwrEsPtwtQutBQjNMmKmHKK3ze680WdclsxkmKA2+Y+EoQZIpQOLZgoVmRJZpOsnaxtGGCQtDRZI0j2ntJWJ86kMHr2uifPaJ+9akZqw/5m5yrzdKtsNupV8YDAaDwWAwGAzvNbbbiPhU98/Pdv/sVWT/NXlNO7mbizJsTpxpPnTfIOcXOv3oxY8dHyFV8Ae//Gj/uK+9Ns3/+/1pkvVREatoxhvkXt4hwlRzeNBjOsl9DTaK4ARNqnL1Qu9jaEB0v46zjB9ON9lXdlhsZSw0NZo8+nO2EeFYgoVWwuRIEUcK3l5sYzuSAwM+Wfd6jxzKC8atduPX794vtELeuNLEkvDy20u04xTPsXj/oQGKjscrby+yEmb9URABpDofKan4DgcHfd5e7KDJR1B6vhN0jS1Xs5nXw0w95elPHN/yHt/Lu+73eqNkO9zIh8NgMBgMBoPBYDBszJaNCCHEh4BLWuv7ut9/BvjPgbeBf2KUEHeeXlF8anK4/9p6ifvXXpvmd//ybH8k4F5lqZ1gW4Io3XidSZb7OhQcSavbNBHd/yWp7qpDNGXPZr6VkGQZBweKTAwW6CSKlXZMPUh4sRVxbLTMB44M0kkUVd/m8nLAg2NlhkpeP3Zws9341bv3UZrywvllBPDgWJmp2TYCwaMHa7i2xenpFVbCDK1BynzkRAGuBNlN59D6WoKGIwVp1z8iiFOiNOPzX3mViYECT50ce1eMMGzGvdwo2Q7v5mdjMBgMBoPBYDDcToTeolgVQrwCfFxrvSSEeIJ8NONzwGPACa31f3FnlrkxH/zgB/VLL710N5dwx+nFd9YKzhpJ+8dPjDI122Z6JeCvz86RpIpWnG6iNLj7WIAlBfEWig3IRzbKnk09SFHd9/WGF6QAW0Cl4CLIU0Oqvs1Q2WOk5PDadAPXyg0i91ULrHRi3n9ogJLv4FqiP8LSK/q3KorPzNR59vQsz70xi2MJHj5Q5a35NmF3lCJVmpJn8+bVJstBggSEAClFVxkhKHkWTz28n785t0iUZHi2IEoVQaoYLblEqWa47PLE8dE1z/UbZ+bXPO9LSx3Gqh7xNte+XXqfsadQ2K3zvlvZ7O+iMZ40GAwGg8FgMBhACPGy1vqDG/7sBo2IV7XWj3a//hfAvNb6n3S//4HW+rHbsN5t815sRMD1BePxsVK/WA2TlH/38jQSjW1LOndx/GI3cGRezPu2lUd8dhHkIxsV38G1BZaULHdifMfi8FCRhVZE0bVoBCmZ1gwUHK7WQxxL8sBYmf1VH8uSOy4aP/+VV7vRmYL/+MZVJDDXjFgJEoaKLmGa0Qzyda6+864leOzwIH/2q6c4M1Pny8+/w9+cW2Sw6HBivMKZmSbNMOXUsaF+JGdvt/2pk2P95+11TTvXG1Xu9HNs9TtkiurtY5o3BoPBYDAYDAbDxmzViLiRR4QlhLC11inwJNc8IrbzXsNtYr2k/ZnnpqgVHGbrAX9zboFMaVIgS9QaBcFeJFUgtMaxNJ6Vf+9aAiEFo2WPdpxhddMyCrZECAFa04pShksulpRMjhR54cIyUkCYZrw51+Ls1Sa1gsM/utrkEw+N8dTJMYAbFpWr5fiWEFxeCYiSjDzJU9OJsr4Z6GriTDNezSX8J8Zr/LNfemRNEZtkmuNjJd6ca/P9i3XKvs3kSJFWlK553s88N4VjW7dkkLh6J3+85lMPEr74V29xfF/ZGC/ukL0+XmIwGAwGg8FgMNwNbtRM+BPgr4UQC0AA/A2AEOJ+oH6b12bYJtMrAc0g5vnzS8SpwhaQaMjukEWEbwvCTXwebpWeOaVjSWwp+eixIT735AP8wdenuLDQJo5jlpIM1U3UGCk7hKnCsfKmxAeO1Dg31yZOM+JuxKcloJ1khGmGFLny4A++PtWPxOwV53/4nQvXKQJW+0WAJsvyVIxqwSZJFUrpDVNIPAv+7p21f2VWF7G//scv8fyFJQTgO5I0U/xdM+LxyaE179mOQeKNduk3SntIMsXVRsh9o+VNz2swGAwGg8FgMBgMu4Hc6oda638G/CPg/wY+pq/NcUhyrwjDPcDEQIEfXKrjWhKrF9NwB7ndppgKGC17DJdcBoouJ8ZrfP7njnOg5tHqNiEEYFuw1EmZb4b8tz95lMnRMo5lUQ9iMq3RGlxL0gjzWM0k1bTjPAZzoRWx1I6pFRykEP1C/dnTs2vW0kt7qBUcmlHGkaECtYJDpiBRmloxL+57iRmOhIItGCq51INkw893ZqbOj642yTKNYwmU0sw3I8I0u+5RTgwUaK5L1lhtkNhTO9SDZE1D5czMtSbI9EpAxV/bgxwuuSy1k03PazAYDAaDwWAwGAy7xQ3HK7TW39vgtanbsxzDTujtfP+HH15hthmt+ZlcFXl5u4lvYfZDstZLYcNjBLTilCceGOH8QptP/vO/4fJyh3aUYQuB70o0uWGkBYCgE2s++8R9fPn5d1hoxSSZRqm88ZCtujFBkjHfDInSDIFgvhlybr6dR6N6FtVViQg9VisZ6kGe1vHyOyvMNkJsmTchBODbMn8OCIJEr0lXWM2zp2expOTIcIHlTkKUKlxbMlhwiNbJWlYrMlZ7OfQSPzZSO/Re7615o7SH8ZpPI8jPtdF59zLGx8FgMBgMBoPBYLi32FIRYbh36e18/+25Oc7Pt6/7+bvpwVoCBgouV+shr11a4dJSB8+WZEoTK02YKHxH4tmCNFMsBwn/+oV3+M7UHJ1E8RPHhqn6NpaETOXKCICyb2MJwVdfvcL5+TYXFpp8++wcUZJR9iwaYcrl5WCNmmA1T50cox4kOJbF+w/XcK3cHHRfxUWIfDRG6Xx0I84yPnPq8IbnmV4JGCo5WFJycLDIsdEyR4aLBKm6TpGwWpExUw+pFZw14yMbqR3Wj1j01l0PEpTW1IMEKSWfe/LYpufdq2xHIWIwGAwGg8FgMBjuLMZwco/S2/l+7moL25LYaKJVPg2ZBt+GNIPbZN9wQwTXmzau/3mlYJNmGqU1QbKxNiJT4FiCH043cCyJ70hsS+JYgjjLmxGLraSvrOj6RvIvvnWe9x+q8djhIZJM8dwbcyit88QNCUmmiBOFZQkcCzoxXKlHBHHGaMVHSsGDY+VNDRt7TYF8tz3lyRP7mG1EHBoq8uZsne9fqhOlmopncXK8yptzHZ55buq6HfmJgQJxkjE11wLAs/PxEceSfRPN9dfdrEGwkdph/YjF2nXnKoFPfeggJ8ZrfHKL53Wn2E0Fw3YUIgaDwWAwGAwGg+HOYhoRe5SeaWGUKjxLIIQkSbN+Ma6BDx4Z4s25FrPN+K6s8Ub9j3x8waJWsZlrxtc1IsSqLx4+UGW2ESLIfTDirtFkj9XvFAIGig5zzYg3ZpqEqWa2ESCExrUECrCE6F/PFmBbNq6dkqSaRpgiZczj9w1yeLi0pWHj+qZAr4geHyjxwaMj18VibmSCmY9bdBiruLw516YZpri25LNPHL2uWO6d//UrdRphSq1g89B4rV+s32h0Y7N13ytslOixkWnodtmOuafBYDAYDAaDwWC4s5hGxB6lt/Pt2ZIwzg0be8W4BRQ8i2aU4Vj37pCGEGBLmG1EROnGaggBVHyLXzl1hHPzbd6aaxHGGUGqsKRAsDalwrcFRdcmSDJcS7DcSQiTjDjVFBybdpwiBVR8h7AVIwVorbEEXbPPfJTi4GCBxXbCvhsYNm60e//0J473X//fv/UWriU5OVFFCmfDHfkT4zU+fmKUL/7VWxRci8GiQ6o0//qFy1xcDPj0qSOcGK/1i/QsU1xeCkDAbD3g4kKHv/j+NB+7f5hfOXVkU7XDXmC3FQzbUYgYDAaDwWAwGAyGO8u9W6UatqQ35z9acUm7TYieQCADXAlnrza5vBLe9rX0ulk7CeuQAlxL0IoV7TjNmwEbHCcE+LbN7z17liffN4ItoRVnaDRZt3dR9ix8W1BwJMNlD9+xiFJFkuk8RQSIM4Vny+61JYprhp6ZgmaYYElIu+9xLcFCK6IeJBuOR8Dm/gNfe226/zo6b3S8/M4KC638WWy0Iz812+Yjk8M8ft9QPlbjWAwUbE5fafQ9DXpF+tVmhOdIbCloBCkrQcJgweb17rEAT3/iOH/wy4/y9CeO75kmBGzP42InbOSHsdUzNRgMBoPBYDAYDLcfo4jYQ/SK0RcuLHJxqUMYpzTDDFvmxbSmG2MpoBkpbGt7rYHeUT1lgdU1WrwRkrxRIGX+hp1YUSgNQaoJ0wTRVSUgQOhr67CloOzb7K/5XFkJ+P9ejXAsSao0OsvjOj1b9hMqWrEiShRCaNCCRGl+/FCNfgimgLJn071U/zMrDVpDJ1YINAdqRZY7KcNlb8uRgM1277/0/EUeGq/m4xgFhyjJ8GzBubk2I2V/wx353gjBCxcaeLbEdyy01jSjtB8j2juml+hxeSXAtQWZ1niORSvK+sfupebDanZbwbCVH4bBYDAYDAaDwWC4O5hGxB6ht/u+3I54fbqOJXOvhJ75Ysm1KHk2lhTMNkKU0nj29hoRxW78ZZIqtAbHgiC98fsU5Dv+mca3JeEm4xWb0W8GdBUdUlxrpvhOPqIQZ4pMKaaXAxCCyZEiSaZoRRmgqfo2K0FKpBSC3IAyVZrJkRIHBws4luSthTZppggShVIarTVxJrEtgS8EnUShASlhpORR8myWOwkPjJa2XP/0SoAt4Y2ZRt4c8G0mR4rMNkIev2+IhVZIJ0q5uNzBtyUl1+rvyK/3bOgV4L0mA0CUKqq+01cE9I4p+zZRkhGnCinyZkyUKsq+vef9D7brcbET7lU/DIPBYDAYDAaD4b2KGc3YI/R236fmWri2RcmzsKVACoHSEKUZtiUQ3XEDDbTiGzcGeuMJjiWZHC1TdCXhNpoQq1GAK9XORjPIkyvo/inJlRiWyBsCUmjCJCNMMt6caxGmuedDwbUZrfikShGniuVOgiVENyozH8H4zSeP8e9/8yf5hUfHeeHCIpeWOri2xLMEqdJkGsJuUwIhKHsWx0ZLHBsps9zJP/xP3D+EY1sbRj2emanzzHNTvPz2Es++PstKO6bsWURJxt9dWKbs2VxcbPPyOytYUnBooECmYKmdkKTZhiqLa1Gggqj7uaNUcf++Ul8R0Dtmf8UjSnIJTJQoyp6dHzta2vP+BzeKJzUYDAaDwWAwGAx7H6OI2CP0ZPntKKXk5jvmlhR4tqQdZ6QKlNIkOxyRcCV4tkWQpBQcSStSO3p/j0acj4RsNypUAUp1Iz712u+lhjjNxyxKnk2kQCtIlWahGdIIk35Uaao02tbUPBvPsWjHGX/x/RmeOL6Pqdk2A0UXOjGZhkrBIdMQpxnQHSmBbjNHY1uS+/eV+ekH961Z6+pRh9WpDiXPYqEVM9+McGyBLXNlyZGhAi9eWGQlSFE6N+Ss+jY/cWyUkYq/ZRToHz//Dt89t8hg0eH9h2s4ltVXBKweM+gkeeNpsR3jOxYnxiu4tnXL6oF7AaNgMBgMBoPBYDAY3t2YRsQeoSfLL3k2UaLwHUGmNLWig9aaTqKYbUbXqRIEW8doSktS8izSTHFmpsnOhivW4tiSLFHYApJtNiQ0a/0oSq6k5NmsdBKsrsLDlpACYZLyzlKKJUXfx0LTTb2w8vEEpTXTyx1+66tvUPVtpBAcGS4hhKATp8w1QxKVKy/yeRBNqjRXGyGzjYijw0XenG2w0L42JlFd5Vew2hdCacGR4QLzzZjZRsSx0TIPHahwYaHDcpAC+dhMpqERZkRpdsMo0N/5pUfWJHHsqzhrPA02iwudXgmoFZw95X+wUeLIXlm7wWAwGAwGg8FguHlMI2KP0JudP76vzMvvLJMpjSWh4AgsCY6Vx1ZaQrPYuTZbcaN+QNrvAmii7ThUbkHFt6n6MN+Kd/xeARRcyWjF58R4hefPL+UNBimpFiQrwTUVhCBPttBZ7jqZKU0zTFFaU3TzkZU3Z5tkWmMLgdIeri25shKiVe/T5v+PU02qcp+MoyNF2mHKd88tMl7zqRUcGmFKI0w5M1PnxHiNN2bq1DsJzSilESSUPZsjw0WaUcpHJoepBwkXlzqU3dyvo2cYGiWKH1yq8w8+fPiG92InioC9qh5YrSxZnThixjAMBoPBYDAYDIZ3P6YRsUdYLcsPU8XFpQ5KafbVCtiWJNOaVphS36HBQ5zpvEDvfu9ISG5CFmELcgPFLFcBSNiRuqJWsPG6owUvXFjGkYJGmOHZ0AwzfFsSp1luqqlgsGBR9m1m6iGZglTlXgmQF/1Fz6ZasJlZCZleCXC7hhSa3BdD6jzmFA2+LagVXR49OMDXX59FCsFcI+TScock1VR8my9+800+9+QDXFoMQOSjFmmmmKmHxJlitOxdi4lUmrGqy9VG3pCxpEAITSvKdj02cq+qCjZLHNnLiR8Gg8FgMBgMBoNhe5hGxB5is93vn/r9b7Gv7BKlisGCSytKaG/DqLJHrwkhACFuNMyxCQLaUZYrFHb4dkvkJpNF16YRZviOxUjZY7mT0IlTMgWBznAs+uacBc/mwEARgWC2GaK1puBYRKkCWzBa8Si6Vlc5Iri4FFDxbWxLEKUa0TXGTHTuMyGA0YpPrWCz1I5YCTJ8R1Ir2CgN355aAA3Hx8pMzbXye110SVJFI0i5f1+5PxoxvRLQCBIODPgsteN8TVowOVLa1SJ7L6sKep4nq9nriR8Gg8FgMBgMBoNhe5jUjD1GL7Hh8195lWeem+LMTJ2xqk8zyuMc8913gXUT5xYiL8qtLeIvbAG+vfbXpugIlMqNKneY4AnkDYEs08RpnmThWII4Vbi26Dc1FLmppiBvmARRRhCn2Jbk0YMDHB+rUvIcpBRM1PzcSyNVjFR8njo5zuRoiY9ODiOFwJW5KiLrmmPaUvRbLyMVn7B7bVsK2nHu7eBaghffWebISIkfPzyA51i0ooySb1PxLWoFt/95PnPqMO0oJck0B2o+I2WPgmvxaz89ufObswWrVQVSiP7Xz56e3dXr3A4mBgo016l39nrih8FgMBgMBoPBYNgephGxh+jtgNeDZM0O+JPvG6EdpaAhiFM6UZYX7hYMFGzkNnM1lYaiI7uqiI1JNYTrug2dRN+SyWWquj4PUYqUksGiS8mzOThYxOkaVgq6TRJLsL/qY1mCVMGpySF+++89zO//8iP82MEaEwMFLCmuxV92Iy3ff2iAqdlWPpYhJbYl8RxJyZVkqhdjqrvRmLl3RDvKSDJFkmqUUoRJRjNMGa34nJoc5rFDNcJYUSu4a57H5GiZL/z8g1QLDnOtmGrB4Qs//yCffGTius++UWNpu0yv5CqP1ewVVUEvirQeJCit+1/v9uiKwWAwGAwGg8FguPcwoxl7iM3m6jux5gs//yDPPPcmFxY7SJkrF+IMomBnnhGZBtcSpGrtfEVeqG/vHDs5tofvWl0fh1wZ0YlTqn5u+BineTyGUgopBWM1n9/+ew9zYry2xiOh4EgmBnxOX2kyWHR47FCtH2n52SfuY74VUQ9j2lGWx3VKQcG1UBoODBSZqYeUfRtLdAM1ug0QhEZpScHNzwV5wf/6lQYaODlR7SsSIH9OT3/i+IaNh9Xc6mhFL0mltirVY6+oClZ7nvT8LXYj8WOvemYYDAaDwWAwGAzvJUwjYg+xeq5+vhlybr5NM0hAwFMnH+WTjxzgwnyL719c5vJKeFPXCBJFYYPfip00FnZyrCsFJ8YrRJnGsyVaa4IkoxEq6kFIwbEQOm+QJFkez/nGlTr//BtvkijN5eWAB8fKHB7OlQ+eY/M/PXWcqdn2dZGWD43XKDoWZ2dbeLbEsyWNMFeS/PYvPsSJ8RrPPDfFcNlluZN0Y0IFiVLEqeLhA9U1xXOSaR6fHGSkfM3rYCeKhFs1bCy6gi9/7wpxmlH2bR4cqzBY8vjUhw4C935RvtuJH3vZM8NgMBgMBoPBYHgvYRoRe4jeDnicZrxycQXPlt3RBcH/+JVXmZptohEktxjDmWS7tOBtcGSkyOWVkOGSi+9YaK1JlOYnjg3z7TfnsaTAtSWdJMO2BL4taccZz19YYqLblDk726Ls2/2GwNRsm6c/cfy6a+URqB0eHCtzbq6VJ49o+InJof4x0ysBR4ZLSNFmoZ0QKYUUUPMdPnJsZM35yr5NO8ygcu21nSgSbsWw8WuvTfPl712i4lnEdu5l8crFFX7jZyb7SpH3WlH+7OlZskzxxkyDVphS9m32VzyTxGEwGAwGg8FgMNxjmEbEHiIvpC9wfr6FZwlWOjFzzYgkVaQ6N/yQ4taaELCz2M1boexKiq7NQiumGSUMlvLkj6rvcGSkxPB0neVOQpBkKKWRUtCJMyyZj0zMNiMmR0pEqeLcXBut4dxci9lmBND3G1itCvj4iVH+07lFmlHG4aEiJ8Yr+I7dL9InBgosNEPacb4OzxZ0YkWiNEVXrCnuk25DCODISK7IqAdJX5FwI25ltOJLz1+k5Nlr3lsPEr75owV+9aceeE/GY75+pc7lpQDPkZQ9iyjJmJpt0bmTnTWDwWAwGAwGg8FwQ0wjYg/Rm6v/h//XCyx1kut+rti5N8N65E2kd95k4CeOJal3YhxLEMaKpXbEUjuhVrD5ztQ8J/ZXeGOmQTtKcazcVzVWGkeIbsNFEKUKz5bMNoK+f8NYxaMeJPzes2eRQnBoqNhXBXzjzDxFR/Kz79u3poiHvEh/6uQYX3/9KsNllyjJCJI8ieSxA1W++aMFHhqv9t93dKQMwEwjwnWsHfsc9BpLkCshdtLImG2E7Cu7a16reBazjXwk51bjMe/1sY6NaIQpCPCdPDPG78a5NsKd+aQYDAaDwWAwGAyG24tpROwxvvLSRZY3aELsFgXHIkkz4i1kETdjRnndOQDZjRr1bEkriplvxewr5yMazTAlKmsODhaYaYSkSuN2fR2U1mgtGCjYXF4OSJUiU5qKl9GJFbNScH6hjcry+M6TE3kB3WsgvHhhiSdP7AM28toY4+BggUaQ0IoyDvo294+WGC57fPXVKzx+39Caz3F4uIRjW/zBLz+6o8/fK/RbUcL0SkDVt3n4QG3bjYyxqk8jSKgVrgXfNKOMsWrefLgVtcVeHeuoFWzqnZgwyfBsSZQqtNbUNjI9MRgMBoPBYDAYDHcN8y/0e5z1O9N/+uJlLJHHaO42Fc9ioOBwpb61lH19E2I7Sym5kk6s+sdqIE4VnmPhaYsfm6gxvRISZ4qCa/NjB2s4lsULFxb52QdHefHtFZTWFBxBEKcEcUa14FD1bepBQpilLLQUjiUo2zYIaMQZwVKbhVbY94+o+DYaTTNMN/Ta+MPvXGBfxePgYPG6sYexqk8zTG85pWJ1of++/dW+EqKnOtiOGuEzpw7zu395Nv9MnkUzymhHKZ/72WPAjdUWW11jr4519MxIrzYiGmFC1Xc4OlzsK1cMBoPBYDAYDAbDvYFpRNzDfO21ab74zbdoxymZ0kgh6CQKN0+53HUypfFdiw8cGeSNK3XCVCPQOJYA8mvDzkcxLAHtdRILDbSijCTNmNxXpug5PHG8ghSif4zSGoFgrFbkqZMe5+baNMIElbksdWIsKRgpe3z0/mG+fXaehVaEEALHzlUClszP1fePmG+z1IrxHck7i22W2jGelR8TZ5oPHMmbH0marYnp7BXxnzl1mG+cmb/u9e16QvTYqtAHtqVG6EWDfun5i8w2QsaqPp/72WP917eKx7yR4uFWxzp2i52Oh/TMSE+MV9c8n55XiMFgMBgMBoPBYLg3MI2Ie5QzM3W++FdvEaX5TrcUol/8xwpcSxDfYjrGeqJUcXGxw1DR5aGJAQq2IEh1P4Hg4mKLq434uiaEZGuDS7FB50ICjiVoRikPdYvL3ihBb1xiqRVT6DYNjgyX+PB9Q/3ishUlvG9/td+48GyJUpoMjdZ5U8WRAgVML3eod/LRC0vCsdESrSijGSa4tqRWcDk5UWWk7KO0ZqaeblrET46W17z+oaMD/3OZlQAAIABJREFUPHt6lj/67tvb9lLYqtDfiRrhk49M9BsPG7FZPOaNrnErYx27xc2Mh2zVfDEYDAaDwWAwGAz3DqYRcY/y7OlZkkwRpQrHktiWIM0UBRuCFNJM37RJ5GZkGhwJ7Tjl2GiZVy6u8P5DAxzpNgDaYcJsIwa4bjxEdP/bqCEhNvhekCseltsJr1+pM1bxuFIPqXg2U7OtftNgsts0iNOMmXraLy6fPT27plgeqfjMNyLCNGOxHeFakqGSi2dbtOKURClGyh737ysxUs4L27wJUt2w4N6siF/9+s16KWxV6N8JNcKNrnErJpq7xc2Oh2z23AwGg8FgMBgMBsO9g2lE3KNMrwQMl1zebLcodFMALCnwXYeyq1kOU9LbkLPpSsml5YBHDw3w44cH1iRCCCEYKTssthPyzIq8EaKAqm/RCK/3lhBAqtY2I2yZv6Z1HuHZCBLOXm3QDDPSLMOx8+s9emig3zSoFRye/sTxNedeXSwXbUGic0PLomuhNSy2Y963v8KRkeIa9UTvPT1/id73Oy24b7ZY3qrQX99ggZ2pEbYzznAjxcO9oCy4V8ZDDAaDwWAwGAwGw+5jGhH3KJ4lWOkktKOUMMkoezZagxSCj94/wtGRMn/4nbeQG/gv3CwCyLSmbNucm2szVLQ5P9/iwnyLIMnoxBkFR1L2LJph1ldjrI/8tMibE3rVy46ErsUEQuTXsiWUfYd6kNCJ8yZGkmnGay5p1xFzoRXy5myL2WYE0C+s1xfLnVTz+NFBljoJM/UQBIzXfB4cqzBS8TcsvB8+UOOpk2M3XXDfbLF8o0L/ZtUI21VobEfxcLeVBffCeIjBYDAYDAaDwWC4PZhGxD3ImZk6V+ohmdLsr3pcbUQst2NKvs37D9aQUnJ8rATkhbsFbJ1zsT00uU9EwVH84NIycar7/g69yM7VTY++IkJDO762AiEFFrqv2LAkeLZF0j0mzqDoWoxXPcq+w0IrwpYSz4YwEdTDhPFagdcu1Um6DYmxikc9SPi9Z89yoOYTZXrNjv/nv/Iq4zX/OrPLmXq4ZeF9KwX3rRTLW41+3KwaYbsKjRtdY6cmkbeDe2E8xGAwGAwGg8FgMNweTCPiHuTZ07McGS4xXvM5N9/uFmEpjSDlpYsr/P0PTjA12+ah8QqvXW5g2wKd6r4/gyBXJaQ3ce1M5bGaUdcAQnclDesjO+Ga2sHu+kUUHUGY5ikbUgjSbtOi4kqiTOPbgkxplAatNStBwnDZI0oVriUJkwwh4P9n715j5LrPO89//+de92Zf2WzeSVEmRcuRJY3NHVvRxFKGWe0ku0GynhcJPICxTnaAYFc7XmP3xUyQxe7CswggDPwiO8IYiCZ+YcQZ5DLrWIHsxKM4oS+yZSkUaZESKbbYbDb7WtdzP/99cbqKfeet2d2Ung9AdrO66tTpKr45Tz3P75lv5+MSYZwy2lcE4OhwmShJGZ/tMNeOeOrY0LJP/DcqCtxqg8TdXnTfr4vluy2O3EmHxnrPcbe5F5ttJ4yHCCGEEEIIIe4PKUTsQN0LSkPZvDVRZ8FPegGPSsPXf3SVI0Nlnn54mIJt8tOrddIsw0Tj2SZjfUWuzbdoRHceZWkZEN7BNg4D8ByTdpiSZFBxLVKtiZIMQ8FwxWH/QJnrCz7XGwGGoXAMhUIx145xrQ6OqWiHCX6cUrBNik5eEOnEGaYBH9vbx1DF48ylWcquSZRmGEot+8T/VkWBtS687/Wie6ddLG/GOMPd5l7cD9s9HiKEEEIIIYS4P6QQsQN1LyijJOWtyQYKMIzF4EetsZTi8kybgeI8l2c7QL4Kc1fRARQzLf+uihCQX3hWCzaXZjq3vK9pQJZBK7yZ72CpFMsy6CvaDFdchioe78/5zLVDlMozLkquhWUYxGnKfCdmsOwSpTGOaWCZBgMlB8NQDJQcLMNgqJJ/yt8KEiwDqt7NC+3uJ/53UxTYjIvunXSxvBkdGhIS+cGyE8ZshBBCCCGEWMnY7hMQq50+OULdj3nrWoNM5+GOALZl5FkNaMIk44dX5mmHKTrTJEnGZD1kphkw176boYycZ5tEt7GOw1Z5d8bSMEqDPJCy7FqM1goMVTw+eXgXnTihHWWkWd6xYZsGaabpL7nUCja/+vG97Co6eLZBkmXMdSJsQ3FkqMh8J6bux2Q6H/lohSlHh0u981i57eH5Z4/x+7/+MZ5/9tgtL7gmFnwq3vJa3IN80d0txtQKNpP1gFrBvuORirG+As1g+f8fCYl8MHU7fup+vKzj5/xkfbtPTQghhBBCfMhJR8QO1L2g/Fd//GZv7aVjGZhK5fkNi6MTBoCpiTKNVnlxoLtO824YQCtK6QQbFzIeGi4wPhcSJhkGUPZM4lRTcA0MwwClePrhYS5Pt/jT1yd5Yv8uXmlPkWYaP0rz7RyLoRO2ldfCfm5fjZ9db1HxLFzLIEwy3p5qc3JPhVrBZmLB55E9VaYaIbZpkml9T5kM5yfrjM91+On4Av1lh6NDJYYq3gN/0X2vHRoSEvnBsZPGbIQQQgghhFhKChE71PHRGs+eGKFgK9682iDNNInOWLKcgjDVhH5eNFCLf93LIs+BskNfweKdTrzufQoWzLcTqp7FXDvCsQxcy0ApjWkadKKEeqA5c2mWdhgTpxmHhso83g754XvzZDqjE2lMw0ApOHWgj7ofc+5ag5WNGAroKzo8/+yx3m3nJ+t87cwVvnN+Do3msX19d/x7dj8pHq261DsRDT/mJ1cWODZSxjSND/VF907LvRB3T8ZshBBCCCHETiWFiB3s9MkRxufyrIa3p5q0wvVzHzT55goFuBaEdzidoYD5TsxCJ+qNWxisLmz0FR3aYcpAxaUZxFiGItMKhaYRJIDGtRRhnDI+67O/P+8uePzgANWCzU/frzPdCqm5Fo8f6OOhkSoAlmlSchWubdIKEsqexfHRCtEawZmdOOMfHervfWJ/p1sdln5SXPYs3rnRZqYVcr0Z8nu/fOKBuOi+n7P/Oyn3Qty9zQgvFUIIIYQQ4n6QQsQOs/IC85njQ1yYKnJoqMIr567TWhybyJbkMyylAcc0KdqKef/2qxEaSBbHJRQ3Rzy62zoyoOwYtMIU18pzJHYVbaaaEUGconW+6tM0FMN9LpB/nywpJDw0UmW4WuCHl+f4zPFhjG74BdBfspltRzxzeKB328qLKNicdvOlnxQPlj0Gyx6Z1kzWgwfiAnynrNgUO5uM2QghhBBCiJ1Kwip3kLXC5b59fprTJ0f4/KcOUnItUp0XITbSiVJqBZv+ot0rJNwJveL7bkGi4tlEaTc0MiFKwbMNHMsg1aAMcG3FbDtmphVyYrSMn2S9sMm6nwdPPravtiwQcaYVMN+JmG6FfPftG9xo+r37nj45suzcNiNgcisCGc9P1nnhlQt88Rtv8MIrFzY1IHBpMaa7xrRWsHn57NSmPYd48G1GeKkQQgghhBD3g3RE7CDrfdr/lW9f5NJshyTJAyJutZgz1XC9EfDEwV28fa3BdOfut2h0aWCmGaIVzLYjAIoO7Co6DJZdri34hEmGZxscHCgRJhlTjYhHRm+GTXbzBoDeJ7VhkvCDS/Mo4NShfq43Qv7unTk+fXRg2UVTt1Pk3LUGF6eaPLKn2lvreadFhNv5pPheRh/ud8eCzP6L2yVjNkIIIYQQYieSjogdoPvp+Z/9dIJz1+pMNwMAppsBr703yys/u8F0M2So4lF1bu8tCxLN99+dY/Y2ihDmbbZMxBqSDOJUE6eapp9gGYowyagVLAyV/6xLA00/5sylWX703hxnLs1yabq17JPaN95vUPEsHt5d5tpCwMSCTzOIeePqzQ6CpZ0iH9tXpRkk/ODSHFON9TsnNnKrT4rvZe3h+ck6v/sX5/iHq3XOTTaYbYWb3rEgKzaFEEIIIYQQDzLpiNhmSz89H6m4NIKEn4wvcGigyOXZDjPNEAMwDbjeCNk/WGa6GTDXjohvsSIjuVXrxKI18iDXZambx03JP53/xMF+5n3FcEWx0Im5NNMGwDXgtfd9Dg6UGC47NPyYL3/rbQCee3SM46M1JhZ8bBO+/+4c850Yx1IYBlyZ7fD/vPw2Xzr98IpOEZtTRxRnJxq8cbXOL57YfVdbHTb6pHijHIru17U6Jbrv5WwrpL9oE8YpPxlf4OP7+xgou5vWsSCz/0IIIYQQQogHmXREbLOlF70PjZR7t3c7AqI0o1qwUSgsA67Od2hHKRrFnj6P22yQuCXjNrsiDJXnRTimQgFpBv8w0aAZRHSilKJrMdZX4PBgiTk/IcvyTAnDMKgVbEquxUtnxnvHG+srcO5ak06U4loGtmmglKLomMy1o95F/9JciMGyx1PHhnhkT43nnz226a3n6+VQvHVt406J7ns5WHaJUo1nm7iWwTvT7U3tWJDZfyGEEEIIIcSDTDoittnKDQ6PH+jj4lSL9+c77N1VwOovEiUZs+2IKE5oRRm2mRclXNMg0WAZ+cjEvVCAZxnYpqIZpuveL1p8HstUGEqRZBkoCOKMkmv1Lr7DJCPJNOXFgkLRyf+rVVyTqUbQO97pkyP86esThElKwTZJ0owkg91VhyjJep0HW7mGcL3nawQJe3cV193Y0X0vjw6X+PGVBSAv2My1ok3vWJDZfyGEEEIIIcSDSjoittnKef/BssdozaNWyNdZKvKuiIGSg59kKPK1mHt3FSm5Fgb3XoQAKLsmSabpROltdUf4UUaUZFiGAWgaQcJCJ+LqfIe3rjWYboWUHQtzMUOiqxmmjFRvBi0eH63xqaMDuJZJkGRYpsFozcMy820c3fGHbhbE0u0bd5ILcSfWe75awdpwY0f3vewWlDzbZK4T0192pGNBCCGEEEIIIRZJIWKbrbzofW+mxU/GF6i5BtcWfH421WRywacRRICiv2T3PrG/eKN1yw0at8NQoJTBcMVhpOre1rpPvfgnyTL8KAU07TBBafBsk5Jj4tmKTpSChizL13i2w4TPndq/7Fi/eeoAJ8dq7Co6DJQcTAWtIKG/5PQyGO5mFOFuV2iu93wnRmsbhkQufS/7Sy7HR6s8ureP3/vlE1KEEEIIIYQQQohFSuvNuJTdHk888YR+7bXXtvs07tnSVZHjcx3SJOHidAdFfqEfpxmgGK642KZBI0gI4hSNJkkzovUnKW6LpcA0FQXbxDIN4iSjGSbYpkGaaSxD49kWdT9ZVvjoFiyUgqpnAnn3Q8mxcKy8U+JGM8RQCqUU+/o8nv/FYzz36Niar8HXzlzh9ffraDSP7evjN08duOsL+KUhoEsDHe+lM+F2jnkvaz+FEEIIIYQQ4oNCKfVjrfUTa/1MMiJ2gKXz/v/Df/wR37u4QBhnGAZYhsIyDOI0ox0mdKKUONNkixUB0wDPguDWWzrXler8L5+MqmngWAo7MdhVtCk4JvVORHux2lFxDFJNb9wi0+BZCss0cRb3gDqWYr4dkmrwLJPjoxWaYUo7XP8kj4/W+L9+9dG7/yUWdQsBr5ybwjYVj+ypYih7VZ7D3eh2SiwtNKzc2CHZDUIIIYQQQgixMSlE7CDnJ+tcvN4kSDIMBVpDkGgMNIaChRUdCZBvrUjvMSOiO2ZhKAjiFNcy6SvYPLavj9lOTCtISLMk35IB7Okr4McpcZrR8BMqnoVlGgRxRtG1GCq7NIME18jDLyfqAVGSgYY/+O6lNTsiNvLv/8tFXjoz3guQ/Nyp/fzWzz+06n5LOxY6YYwfp7w322aw5FBwLNJMg+K2uhTW62yQQoMQQgghhBBC3BvJiNhBXj47hWOZvZEHnV83kwGJZlPyINajNYRxRjtMqfsxJccg0Zrf++UTfOqhfFXmkeESrmky04rwLEUQZ1iGorA4itEJYxp+xM+uN2mFKa0gxo8S0lTjmAaG0lyead92VgPkRYh/95136IQpVdekE6b8u++8w7//LxdX3be7PjNKUpphSprl/8GvLvhcnfdJ0gzHNJat3FxLt6Cx3ppOIYQQQgghhBB3TwoRO8jEgo9pKEYrDpq8ALFVCR7Z4h/TgH27PBzb4v1Znz86c4VaweaRPVVMw6DiWURJyvvzAQr4uX01jg6XKToWrm0RpxrbyI+TauhEGY0gZsGPaYYprql4+ezUqudfL1jypTPjOKZJyTUxDIOSa+KYJi+dGV91jIkFn4pn8c50m8Fy/hqGi+0ipqGY68ScHKtSK9hrnkNXt6BRK9gYSvW+3+gxQgghhBBCCCFujxQidpCxvgKOZTBYLbCn5m7Lm1Mr2Jhm/szHRsq8/v4CFc9iqOJxaKBIO8pHNyqexdMPDzFULfCvfvEYzxwf4cBAkY/srvLIWB8HB0q9YkqcZGRZRpJmeI7JW9eWdxZs1IFQ92MK9vI9HqahmW6Gq4oW3fWZrSChVrDZ0+eRZXlXiWsZ1AoWg2Vv2crNtXQLGkvd6jFCCCGEEEIIIW6PFCJ2kNMnR+gvOcy2IuqdBON29mgCrnmbd7wNdT9mfC4fYyi6JgrVW1k524nZu6vA/oEio7UC1xshb15d4Hf/4hznJuuESYpr5f+lBspu77wywDYNDgyUqBYcGiuSNTfqQKgVbPz4Zl9IlOS5FI6pVhUtuuszbVMRximGUpRciwMDJYYqLkMVD1i+cnMt3YLGUrd6jBBCCCGEEEKI2yOFiB3k+GiNL51+GNdS+EmWX8CveIdWlhwUgFKr7ne3Ug3ojMm6z5++fo3L003+8xvX+On4HE0/RmtN3Y9ph/kK0f6izVwr4v1Znyy7uU0DwLXzTRpDZZfjo1VKroXWmlphebfBRh0Inzu1nyhNaYcpWZaPeWTAY/v6VhUtulstHtlTZd7PCwlPHuwjSTXNIOHwUJG6H1P3Y06fHFn3NegWNOp+TLb4+97qMUIIIYQQQgghbo9szdiB6kGCZShMQ1F0TFphShjnhQmlwLMM4sV/O5YiTLJ7ypKwVB6GCXlhI07zbAUDsE1Fw4/424uzuLZiuOLRiVKSTBOlGWXXor/ssLvi8g8TdZJAo7VGAYZS9BVthiouzTCh6tkcHChycLC87PnH+gq9jRhd3Q6E7naM7tYMQyk+cbCPxw8O9O67dGzi+GiN//tXH1229eITh10UEKaa4Yq9auXmSrezplMIIYQQQgghxN2RQsQO0s1KSDNNkmYkGfhxXhCwDEApqgWb4YrHbCskThKa4b0VIWB5ESJbsp0jA6JUU3QMkkwTx5rrzRBbKSqeSRhnNIOAI0MlDgyW6MQpIxWX19+vo9GcOtxPlGr29RepeBbNIFmzs+D0yRFefPUywLL7ffbJvQD81s8/1CtIvPDKBep+vOzxa41N3OuazQ/ams711pEKIYQQQgghxFaTQsQO0s1EGCg51Dsxirw4kOq8WGAZmpGKy5OH+rky2+b18XlcyyCO0k15/rUKGhpIM41lKBKl8UyDVGuCRFN0THaVbGbbMcNBwiN7ajz/7LFlj196AeyaioJt8NXvvbfsYnijDoSVF9DHRkp8+/w0sHbR4kGyVcWBboGrVrCX5Wp84alDUowQQgghhBBCbDkpROwgEws+ozUPzzYpOCZ+nJLpvBjRzaOcbUdEScqXTj/M5//wNbTWtDapELGeKNW4Kh8LsQxFf8HBtU1cy8AxFTOtcN1iQLfQ0Ov2SDOuNwJ+Or7AX529zu985gjPPTq2ZgfCWhfQ3z4/zTPHh7gw1X6gxya2sjiwNAwU6H3t5moIIYQQQgghxFaSQsQO0s1KSLXm4GCRy9NtgjjDMKCvYFEtODx1bJhaweb4aI2ibTBRDzb1HEzykYyl3RGZhjjRFG0DpRRDVY+jQyXemW4z14oo2Gav08ExVS+PYemn/C+fnSJNMy7caOFaBv0lm0aQ8JW/fpfDQ+U1L4jXu4C+MNVe1XnxoNnK4kC3wLWUrCMVQgghhBBCbBcpROwA33xzgpfOjPP+XIcoySg7JrWijWubFByL0ZqHaShc26TiWbx1rc6//KPXGJ/3idL1EyIM8qLCnVAKPFOR6byY0OVYBqap8OOEyQUftGZ31aPkmBhK4VgmmU74waU5NPCJw7uWfco/seBzvRHgWgaebQJQ9Sxm29G6F98f5AvorfzdNgoDFUIIIYQQQoitJus7t9k335zgy996m4Yfs7fPo+pZ3GgFXJxq4kcpOssIk5QwyTg6VOLKTJur8z4/uDyHoVav84R8jMNQUHKMNX++kUSDn2jSTGMb+fEX6wZoFE8e7Ge05jHbjrhwo0XBMtjXX6RWsLk006HsWVQ8i0vTnWWrNcf6Csy1Y1zr5n+5MMkYKDm8da3OC69c4IvfeIMXXrnA+ck6kF9AN4Nk2fl9UC6gt/J3k3WkQgghhBBCiJ1EChHb7KUz45Rci1rBxjAMSq6FbeSdEEeHSvhJxvicz0wz4Dvnb/Cd81P4UcKCH6MUmIZa9Sbqxb88x7rjjRrdLIpEQ5LB3j6PE6M1PNuk4tl8bN8uTh0Z5L95dA+fPDzA5bkOFS9vrGkFCa5l4FoGjSDfbNH9lP/0yREsQ9EIErTWBHFeXCnaBlfnfep+vCwr4fxk/QN9Ab2Vv1s3DLRWsJmsB9QKtgRVCiGEEEIIIbaNjGZss6lGwHDZ6f17rhPh2Yow1Xxsfx/tKGWuHdHwEwqOSZRqri34pIu5DYahVo1fZBpsQ9EMots+D0VewMj08ttSwLNNkizvkliq4lkoFM0goVawKXsWYZwHZ1a9fAyg+yn/8dEav/OZI3zlr99lth0xUHI4OFDk7estHt5dXjMr4flnj627TeNBt9GmkPv1fB+E100IIYQQQgjx4JNCxDYbqXo0/BjLyJjrRMy2IhSaimfzzo021YJNmKRowDINLDMlTMA2IM7ASPWaORBxponvICBCr/gKeb7EdCOg5JjYpoGxYs6jGSQ8tq9G3c+7Hw4PFvnR5Xk0cGJPpfcp/5MH+3jhlQtMLPg8vr8PTb6JY6yvQCdO2T9QYroZ8M50m1aQUHZNqosFic28gN6qdZm3S4oDQgghhBBCiA8jGc3YZp87tZ/5TsT4bIckSVFKk2R5R8N0Mw939OOMOM0I4oQ002gNSiksxR2PXnQZ5B0P3T/riTOYa8cc7Pcoe/aqUYLfOHWg1/afZHBspEzJMfn7d+c4N9ngI7tLfPv8dG/0wrZM/Djj8586yPPPHuPEaI0rM21+Mr5AGKeUXZNGkHB13u9lRWyG7rrMtUZAhBBCCCGEEEJsHSlEbLPnHh3j8X19FByTMNVUXJvhskO16ODH6WKmAkRJhtZ5V4RjKpJUo1S+zaJo393bqBSUHQPHWr8UYQAV16QdaX7nF46smTNwfLTG888e4/OfOkiU5uMiBdsgjFO+/sOrZFmWZ2AotSzAEvKshAtTLQBcyyBM8jaOh0fKvftshqXrMtc6DyGEEEIIIYQQW+O+jWYopTzgVcBdfJ4/0Vr/rlLqEPB1oB/4CfCbWutIKeUC/xF4HJgFPqu1fu9+nd9OEmWaAwNFWmFK2bMYKNqMz3doBgmNICaKM1Kdb5kwlcIwNIZB74I/Su90SWfeSeGZiqJrUTEU1+vhshGPXreEgmaYsrvm8dyjYzy3wTG/duYKl2faVDyLqmcTJhnTrYh3b7Q4OFju3W/pmsrjozX2DRSod2KaYULVszk5VqW/5N7VKsv1xi8+yKtAhRBCCCGEEOJBcj8zIkLgF7TWLaWUDXxPKfUt4H8BXtBaf10p9f8Cnwf+YPHrvNb6qFLqnwP/FvjsfTy/HeH8ZJ2r8/nFcHUx7PH89YAkyRirFQiSlMl6gKU0mYZIa1ylqLh5gOTR4TLv3GjRWTMpYm2mAsc0KNgGpmHgxymupfKqA6DJxz+6YZjNIObSdJvzk/UNMw1ef79O2TXxFvd9erZJ0TGYqAfLMiBsU/HInmrvcSdG85yJblAlQN2P73iVZXf8olawl41ffOGpQ4z1FVY9xwdlFagQQgghhBBCPEju22iGzrUW/2kv/tHALwB/snj7S8B/u/j9ryz+m8Wff0YptVF8wQfCy2eneHgk7xYIkwzXMmgFCa0oxbYM+ooOgxWXvqKDa+UdEKnWtMKEIMl4f84nuY2OCFPlAZeOqfBsA8c20EpRLVjs7y/yXx0d4n9+5iiuZRInOi9CKLAWEypbYcrv/9WFDTMVNHpV3kTNswiTlB9cmiOMEiwjLwBMNcLesTZrleVG4xcf5FWgQgghhBBCCPEgua8ZEUopUyn1U+AG8ArwLrCgtU4W73IVGFv8fgx4H2Dx53VgYI1jfkEp9ZpS6rXp6en7efpbYmLBZ/9AiccP9OHZJhPzHeY7Ea0w4dxkgzeu1rm2EDDTjggSjW1AmuVhlTXPIs0yOnGGeYvnSfXN1ZwF26Tq2Tw0UmHvriKubWIY0Ik0z310N0eGyjiWgQbiNEMp8GyDmVa4YabCY/v6aIUpQZyitSaIUzKt2FMrUPYsokxTcCxOHelnX3+xd6zuKsu18ifu9LWseMubfLrjF5v1HEIIIYQQQggh7s19Xd+ptU6Bn1NK9QF/Chxf626LX9fqfli1FEJr/SLwIsATTzxxt0sjdgzXVLx6YZoozYjjlAU/wTIVcbJ6LacGGmGGqaBWsHEsgyTVuJYBGtIsI9qgOSLTYGiIU82gY/G5U/v59vlp9u4qUvEs6n7MtXrAbDsiyzIcU6EMRZpBkmnqfrRhpsJvnjrAZD1grh3RDBIcy2D/QBHLVHxkdxVjSYPLjabPK+fmlmU5PP/ssXt6LW81fiHrMoUQQgghhBBi+23J1gyt9QLwXeCTQJ9SqlsA2QtcW/z+KrAPYPHnNWBuK85vu5yfrHOtHtAKEhxDca0eECUptmlsmPiQaphuRUwuBDT8iNGaCwocy8Qx8zfVXKOso4FMa5JM8zufOcKFqfaqUYYDAyWU0pimQZJBEGVESUYnSphrRRtmKhwfrfGl0w/z9MPDPDIr1R/iAAAgAElEQVRW4+mHh/nS6Yc5MVqjGSS9+820An5waR7bVJu6SlPGL4QQQgghhBBi57ufWzOGgFhrvaCUKgDPkAdQ/g3wa+SbMz4H/PniQ/5i8d9nFn/+11rrB77jYSMvn53iwECJ0ZrHO9Nt4kxjm4qCbdIK01s+PgP8RHNlxidjRfuIzltMVr6AqQY/yo+93iYJxzTJbM18nGCo/DhpBq0opehsHNuxXtfBi69e7h3/7EQDBTyyp9orgHRfj3vpWOiOXyzdmvHZJ/dKF4QQQgghhBBC7CD3czRjFHhJKWWSf0j/x1rr/08pdQ74ulLq/wReB766eP+vAn+klHqHvBPin9/Hc9sRuoUAQ9kMVTwm5jt5vgL5C5Zxs5hgGxCv0yaxXslivSpOBvzrPzvLJw8P0AysVaMMe/uLvDvVxDa7uRIKz1I4psF3fjbDb/38Q3f0e64sEERpxpOHdjFUuVkE2axVmjJ+IYQQQgghhBA7230rRGit3wQeW+P2S8A/WuP2APj1+3U+O9HKTIOP7a3x6sUZPMtkoGwz3YrR5MWI9YoQd6pb2PDjjCtzPq6d/xeoeBbNIKHux3zu1H6+9Cf/QF/BxjJN0iwPnrRNxVvX6rzwygVOnxxZ84L//GR9WUdC935LCwQvvHKBuh8ve5ys0hRCCCGEEEKID4ctyYgQa1uZaTBcLXBitMrumkfRtRmpODgr1mEowLiDpaZqydfum20qyLJ8BehamySee3SMQ4MlMq2I0oxM53MeSQY1z+5lOnzzzQleeOUCX/zGG7zwygW++eYEL756mbofb5j9IFkOQgghhBBCCPHhpR7kGIYnnnhCv/baa9t9Gnfkm29O8NKZcaYaASNVj898ZJBOpFd1ECx1frLOr/3BGbTO6MT3/n7ZBlimwUf39vHHv3Vq3fP88rfepuRa1DsRfpwXJB4dqxJlMLng04lS/vGRAQ4MlmgGCd9/d5aHd5c5OFjuHafb8bFyI8Z6nRNCCCGEEEIIIR58Sqkfa62fWOtn93V9p1hu6cX9cNmh4cd87fvvc2SwwGvjdYI446vfu8R//8QY/+affbT3uOOjNQ4OlrheD8h0TJDcLEZ0syTuRJxBwYbPndq/7n2ee3QMgJfOjHNltoNpaAqWwfnrLQbLDkmaEacZF260KHsWQxWPJNO8c6PFZCOkFSSUPYvDg0VaYb4xQ4oPQgghhBBCCCGkELGFXjozTsm9GQ5ZKxhcr/t896KPYypcE6Ik4w//fpyfXFng6Ei1d8H+L58+zP/xn8/hWCbVgkGSavw4pexaTLeiOz6XOM24MNWGNye4MNVeszjQLUb8mz8/R5xm+InGMjJmWhFxmlJ2bVzL4J3pNkMVD9dSXJnrcGSoTNk1CeOUH12e5xOH+zk/WefFVy9TK9jLxja+8NQhKUYIIYQQQgghxIeIFCK20FQjYLjsLLut7ufdApapiFNNmmkyDWcnGnz62DBvjM/xte9fwbEMokSTJCn1JMUwDMb6CijFXRUi/AT+9CdXCdOMTx7qJ0xSvv7Daf7D317i0GCJ//HpwxweKvOV77xLxTVphtAKE+Iko+BAnGrKroVrGbSC/HeIU41lLI8d6YZtvnx2ilrBXlKE2ZyVnUIIIYQQQgghHixSiNhCI1WPhh9TK9y8WO8OWURJhkL1/p1o+MO/u0wrTDEUVDyTOIU40+zpKzBS9QiTjHduNO/6fGbaIZYy+PGVeRpBgmMalByD6/WAL3/rbU7uqZJkmuGqRzlKuTLbJkw0SabpLzoYhqIRJFQ9i7ofEyQZpw7vYq6T0Ahiqp7NiT0VwlRzbrJOvRPTDBOqns3R4RL9JXdTVnaCjH0IIYQQQgghxINCtmZsoc+d2k87zFdkZlm2bIWlQqEULM0ObYYpmvy2RpCSZhqlFHPtCKUUnm2San3Xb2KUZHi24nojwDENlIJOnNEME/wo5e8vzdJfsgmTjJJrcWCgRMWzMZRibFeBh0fyUMrqYqfDp48OMFIr8snDA/ziid188vAArmXhmor3Z30aQULFtQjilB9fWWB8tr0pKzu7Yx+32tYhhBBCCCGEEGL7SUfEFloaANndmvH0Q/189+IcaaYx1M0OiaUyQGmI0gytIUpT3ry6QNEx0Zmm6JjEaUqY3tn5KBSphjSDdhgTpRqlwDYVaZbRDBLem2mRasVg2aFWsHtjGrWizcHBMr/99JFe50G3IABQ8SyaQV50KdgGx0bKXLjRIkwyXMsgTDLenmrx208fuYdXNCdjH0IIIYQQQgjx4JBCxBZ77tGxXkGi61e+8recm2yQ6DxPYa1iRLczAvL7ZFrTClOUUhgGRGvERKjufdc4ngE4lkEnTFAKwlTnz71YmGgGCbahSLWi4prMtEI6UUrJsfhfTz+86neAfLvHF546tGxE4rNP7uWr33uPA4Mlyp7FO9NtWovjHNWCvSmFgokFn9Gat+y2imdt2tiHEEIIIYQQQojNI4WIHcAy8pWat8tUYBt5mSFIMpIoXb94sc4x8p9pyp5NzYCpRoRS4JiKKNVoYH+fB0oxWPGwWhH9ZYff++UTGxYPjo/Wlv38/GSd8bkOr4/PM1h2eWi4xGA5H5/odi7cq7G+wqrjNYNkU8Y+hBBCCCGEEEJsLsmI2Gb/4qtn+PH7jTV/ZpB3NKyUaggTTZCk2AZk61UbNmCq/ALeNBT/5CPDHBks4tkG2eIzFm2DSsGh6OS1KttUNIN4o0Ou0h3V2F1xsQ2Duh/z2nvzvDfTou7HnD45cucnvobTJ0eo+3GevaF17/vNOr4QQgghhBBCiM0jhYht9urFOWDtgkPG+h0NcaZJMoiyuytEZMBDw2VGqh7nrjX5xJEB9vWX+MjuCgNlB8MweG+2zXszba7MtEnSFMc07igEspvdcGiozMcP9FEt2KQZTDZCvvDUoU3Lb+iOhNQKNpP1gFrB3tTjCyGEEEIIIYTYPDKasc26Exl3UktYet/sDkY6lj2vhh9cnueJA32cv97CNk0e21/j3LUmSZaRZhmWYVB0DNJMM9WM+NRIhVrB3jAEcukazXPXGnxsXxWwGap4DFU8Mq2ZrAebXiRYORIihBBCCCGEEGJnkkLENjNYO0zydjmWwjYUrejOj9IJY643Qj51dIBawWZiIeHph4eZaQa0woS/e3eWVGs822Cg7DDTjjkyXFk3BLI7ilEr2IzWPC5ONfnBpXlOHVEMlvMwyZ2e3bC0kDLWV+D0yREpcAghhBBCCCHEJpLRjG321EP99/T4obLLwcEy5l08Nkw0852Y3zx1gOefPcbv//rHeP7ZY4SpZv9AiSNDZfbuKrJ3V5FawaYVJBsWEpau0TSU4pE9VRRwdqLxQGQ3dAspdT9mtJYHat7JKIoQQgghhBBCiFuTQsQ2+8PPn+LpuyxGGMCeXUX29xdw7bVSJm5BwaePDqz6xH+sr0AzSDg6XCJMMoI4JYxTbFNtWEiYWPCpeDebbIYqHk8e2kWUZg9EdsPKQkr3+5fPTm33qQkhhBBCCCHEB4YUInaAP/z8Kf6nzzzE08cGKdoGinyrxUZvjqnAtgz6ixZ//+4ccQrWYi1CsXb45bLHGmCbBr9x6sCqn3e3UHRzIwDm/YRH9lQ3LCR0CxhLebbFL57Y3eu22KlFCFhdSAGoeNa6oyhCCCGEEEIIIe6cZERss2++OcFLZ8Z5f65DlGRUPJNDg0WuzHZW5T4o8gKCMgwsAx7b28f4XJAXHpQm02AbikzrZZs0ujkU3a+pBkPBkwf7AHjhlQurMhG+8NShxayEPDfidrISTp8c4cVXLwP5BXwzSKj7MZ99cu8Dkb0w1leg7sfUCnbvtp2eaSGEEEIIIYQQDxql9V3sftwhnnjiCf3aa69t92nctW++OcGXv/U2QZww34lJFusOlgFplm/HMFVeOFhpb5/H//5ff4S/eXuG8dk2r4/PE6+TV2mQH0tzs1NiT81lX3+RhU7CsZEyBwZLvcLBvYxPrFVwAHohlksLFDttTGNp2OZOPk8hhBBCCCGE2OmUUj/WWj+x1s+kI2IbvXRmnCBOmG3FqMVxiTiDJMuLB4q1ixAKuNEM+dd//hafPNhPmqbrFiFWPs5QYJkGjx/o53ojAAUXbrQoexZDlXyzxUbrOW9lrTWaL7xyoZe3APS+3svz3A/LO0HyQspnn9y7o85RCCGEEEIIIR50UojYRlONgPlOXoRQKzofMtbPedBAnGrqnZgLN1rMd2IKlsJP1u9uMRYPlmnoL1gcGipzcbpF1bMIk4x3ptsMVbz7kokwseAzWvOW3bZTsxfWKqQIIYQQQgghhNg8UojYRiNVjyuznXz8YnEUYylNPqaRrNHtoMkLF9cWAmpFG8d0cOKUKMmI04xM50UHC0jJv3cthWkoosUAiapnE8QprmXQWgyZvB+ZCNuZvfAgZFMIIYQQQgghxIeJbM3YRp87tR9jsRNivV6GtYoQSwVxyr7+Yj7GkeUhlZ5tUnHy7RuGqSg5Bv1Fm7Jr45lGL8iyu56zESSUXZO6H2+4nvNudbdw1P2YTOv79jwrdTMf6n7MaM2j7se8+Oplzk/W7+vzCiGEEEIIIYRYnxQittj5yTovvHKBL37jDS5MtXnqaP+a91PrfL+SoeDknjJTzZAoyUiSjFaYUg+zPJxSQcm18WwTBUSppq9gU/dj+ksuD4+UAaguZjjcj2DGbvZCrWAzWQ/u2/Os9PLZqV42haFU7/uXz07d1+cVQgghhBBCCLE+Gc3YQku3MtgmfPftG8x34jXv21+yafgxGTBQcrjRjNa8n2EoXn7rBlXPZMFPV3VWhIlmphVSck08y6RSsPniPz3Ghak2Ews+BwfL/PbTRzg+WuP8ZJ2vnbnC3787SzNMqHoWpw4P8BunDtxz0WA7shcepGwKIYQQQgghhPiwkELEFup+Qh+nKa+P13Etg4m5zpr3nW3HWIv9KpmGimvQDjOUujnGoTWUHJOpRkiW6XXHO1INnSjDMgyeOFDjuUfHeG7Ffc5P1vn9v7rAzyYbtKMUy4C5VsR3L0xzvRHyxX967IHLVtjObAohhBBCCCGEEGuTQsQW6n5C/8PLDVzLwLNN0g3un2QwVnMZrHjUOxEFO6URxASL2zEsIx+paIUJt9remWaadpQw0444P1nvdUB0gxzH5zrU2xFJpnEthWUamIYmSfOOip22avN2nD45wouvXgbyTohmkFD3Yz775N5tPjMhhBBCCCGE+PCSjIgtNNZXoBkkNIIY17r1S28p8OMMzzapFR0qnoVnWzimouwalByLVphiGQq1UZAEeZaEa5nMtCK+dubKqiDHuVbEtbpPmKSYi7s+TUORZpowSR/IcYbtyqYQQgghhBBCCLE+6YjYQt1P6B3TIIhT1C2qB91uhyBOcUzF9UZIkmm0hiDOKLsGSZoRprq3CWMttqFwLEXZsyi7Jq+/X2ewcjPIEaC/7LDQifDjhDQzsMy8CGEaCtcyH9hxhu3IphBCCCGEEEIIsT4pRGyh7if0Xztzhb99Z5ZdRRtXQbhOEaETpWQa3p1uEcQpSQZlx8AwFY1Qs+AnvfsaKm9vyTTLxjTy2zRRCnGSEcQppmmsCnI8OlTiRiOgHcaEiSbNUpJUU/IsBsvuba/aXDruMdZX4NhIqReMOdZX4PTJESkMCCGEEEIIIcSHmIxmbKHuRXqYaj59dIBH9lT5Zx/fu+aboIAgyfLRiMUihLF4W5hoHHPF/RcLEAoo2AafOtJP2THyFZ5AlmpmWhHvTLdxTdUbE1nKNhWubaLRJJmmv+zw9LGh2w6qXDnu8d5Miy9/620uT7cYrXnU/ZgXX73M+cn6Xb1+QgghhBBCCCEefNIRsUWWru4crXm94MQvPHUIyIMnv39pjk6U0AwSkjQjzsAyFK5tEIf5as4kgwTNyoiJFHrrNMIkY6Yd87G9fZybbFD3E5QCU4HO4OKNFr/00d2Mz+WrQ4M44UeX59HAL310N65l9c7tTroXultBuuMe1xshJdfiejPk0FC5d/uDGHwphBBCCCGEEGJzSCFii6y8SF96Ue6YireuNTAUWKaB1rrXyQAQJflujaUTHMkGazIyDQ0/4tqCj9Y3V37qDEwDwjjjP/zte/zc/j4mFnxuNHxqBYeTY1UGyzfHNe60YLBy3KMRxFRck9aSzouKZz2QwZdCCCGEEEIIITaHjGZskYkFn4q3vO5T8SzeulZnqhHSDBLKrkXJNkg1pNniSIXWGxYd1qKA+XYEQCvM0Jr8DxBneaFivh0xWHY4MVol04rjo5VlRYi7KRisHPeoejbNMKW85PduBskDG3wphBBCCCGEEOLeSSFii6yVyZCv8kzY11/k1JF+Co7FjVaE4mb3g9ZsuBFjLaYBQaLJsgzN8vBKFo/t2iaXpjvUCja7ijbnJ5urzu1OCwanT45Q92PqfkymNburLu0wYXfFJdO697PbDb4UQgghhBBCCPHBI4WILbLyIr37fa1gUfEsBssetgGtICFO88rDWvUHBXiWwlxj86cCTPLihaEgTDRFW606lgZ211waQZ4RcWJPhfnO6nO704JBdytIrWAzWQ84OFjmf/ulhzk0VGayHlAr2HecOyGEEEIIIYQQ4oNFMiK2SPcifelqy88+uZeXz05R92NuNHx++F4eGGmom10QlqlIM41jGoRphiLPkVBKk+n8TmGie10U3dDKgpWPYYz1FZluhbTCpNddYSoouzaena/ecC2LTx8doFawl53b3RQMjo/WVj3uubt90YQQQgghhBBCfOBIIWILrXWRDvDiq5f58ZUFAGwjLyDYpiJONWmm8RyTPs9mth2RZhlRkmEoRX/JoRUkKLoFiXwIw7Og5DoESUonThmueJhGnhkRxgmGUjSDhBN7Kr3uh2eOD3Fhqr1Fr4QQQgghhBBCiA8rGc3YZt1OiSBJ0VoTpXnXQpJpTCPvchgqOzz98BBHh8uM1DwGKy57+jyUUgyWXY4Olym5FgMlh6GyQ6XgMFhxeXx/H45pUHBMKp6JaUC5YPOPjw5y6nA/cZpv73jm+BDfPj9N3Y8ZrXnU/ZgXX73M+cn6dr88QgghhBBCCCE+YKQjYgf4t395jrq/PMhSa0g0VFyDP/iNxzk+WuP8ZJ0XX71MrWBT8Sz+8h8msQ2DR/fWaIUxUaKJ0gw0fHx/HwNlF9syeWRPrTdycfrkyKqujBdeubDualHJcxBCCCGEEEIIsZmkELHN/sVXz/Ddi3Orbu+GS4Zxxstnp4DVORMDZZfdFZehisdItUAQp0C+EWOoknc2PLKnxvPPHlvzuc9P1nn57BR/9tMJRip5Z8VQJV/heTfrO4UQQgghhBBCiFuRQsQ2e3WNIsRSUQZf/+E4Z96d4fd+5ZFlORPdDom6H3N4qMgPLs2jgOOjN7MfPvvk3jWPu7S7YqTi0ggSfjK+wMf39zFU8e5qfacQQgghhBBCCHErUojYYt0uhO6oRHYbj0kzzbnJBl/59kV+55mHlj2+GzI5sZBw6nA/GohSTa1gb7j54uWzU71xjIdGyr2wzHdutHAsc8MihhBCCCGEEEIIcbekELGFlnYhdEMhb8dsO0IDf/nWFOPzHZ44ONB7/LfPT/OFpw6tKjh0Cx5f/d57a2ZDTCz4jNbyMYzBssfjB/p4Y3yBt6daNMOEx/b1bdrvLYQQQgghhBBCdMnWjC20tAvBUCovSFRvXQvSS74/P9nknRuN3uOzLON3/+IcX/zGG7zwygXOT9aXjWystwVjrK9AM1gekNmJMw4OFHnm+AiOZcrmDCGEEEIIIYQQm04KEVtoYsGn4i0vPPzaEwcZLlmr3oi13hgFmIbip+/nxYGZVsDPrjeZa0XLCg5/dObKqoJHrWD3Qi8BTp8c6eVIZFpzdqKBAh7ZU133MUIIIYQQQgghxL2S0YwtNNZXoO7HvfWYAM0g4ZlHRlnoRPzNhRnSJMOxFHGqCVO97PEaSDJNHKWcuTRLJ0xQSgEZ/+knV2mHCa5lYBsGnz42yLnJBq0goexZHB4s0gpvdkCs3MARpRlPHtrV25oBsjlDCCGEEEIIIcTmk0LEFjp9coQXX70M5Bf5zSDhymyb6WbAezMddJoSZRBFywsQCjAVZECm826JMEp4b7aNZyk6UUbBsSg5JkGcMdcO+etzU4zuKlJ2TcI45UeX5/nE4f5lx126geOFVy6syqyQzRlCCCGEEEIIITabjGZsoW4XQq1gM1kPqBVs9tQ8bjRDQBNlYKi88NClAMdSKEUvLKLsmkSZxjUN5joJSaaJkpQk09imwlRQD9Nlz61ZftyVVo5qdL8/fXJkU18DIYQQQgghhBAfbtIRscW6XQjdrRbfe3eWhU5MmmkMBYZSaK3RKJJMU3IMlFK0kryw4FqKONM0/Jg4zdAaLCNf8Vn3Ywq2hWspMhSebdIIYqqezYk9lVWjHivPa+moxlhfYcP1n0IIIYQQQgghxN2QQsQW6hYf3rpW5+q8z8MjZUYqLlMNnyhlseOhWyzIv7airPd4x6DX2jDXjrBU3imRZmCaCkuBbYI2LExD8cnDA73HXp5ucb0Z8sVvvLHmOk9YPqohhBBCCCGEEELcDzKasUWWrtRsLGYxvD3VYrDs0F90N3xsd6QiyiBKNWXXJM00idYMle08xDJNaUcZ062YmXZEmqa9MYvL0y1ef3+B0aq77jpPIYQQQgghhBBiK0ghYou8fHaqtxKzFaZUPQvXMphtx5Tdjd8GtSTcQQMahWMpLENxYKCMoXXeUbHIBOb8hNevzDFZD7jeDPn4/j4ODpZlNacQQgghhBBCiG0lhYgtMrHgU/HySZiyZxEmGa5l8PZknYs3Ous+bq2AySNDZcb6ijiWSSNIiLL8fpYBRceg6JpYhuLdmTa//+sfo+pZTNYDXjk3xZlLs0w3A1nNKYQQQgghhBBiW0ghYouM9RVoBgkAR4dKhElGI0hoBMu3WxgrKg+afGXn0ptvNAIUio/uqfTuYypwLANzsX3CNiCIM85P5nkUjSDprfL8yfgCV2basppTCCGEEEIIIcSWk0LEFlm6HnOg7HJsuAx6SRHBzAsI2TqLLbo3mwpaYcLe/gJ7dhV5fH8fprH4uCWPjTPwbIOXz07x8EiZIEq4MtthYsFnuhny5tUFWc0phBBCCCGEEGLLSSFii3TXY8ZJynfO3+DidIuPH+ijYBsYhsK1DDxreTvEyjdHARXPwjQV/91je/DjDNsyOTFaIQP8OMOPUpphSpRqirbBDy7NUHBMlFKARmuNaUCQZgghhBBCCCGEEFtN1ndusU6c8Y8O9VPxLJpBwkjF5cq8D0mGbeTFBg0Mlyw6iSaI097KTscyMQ2DLNO8dGacE6NVagWbf/KR3RhK8ebVBsliV4RnQpDofAxjtsNw1WO46gHkxyQP0Fy5rrO7YnRiwV93zacQQgghhBBCCHG3pCNiCy3dnNHdXvHzHxnhyGCRTGvasUaTV4d2lT0sQ6F1PnaRZhAlKZ0owVQw1Qh64ZcAj4zVKLoGpoKCbVB0bcquhWsaTDVC/ChB67ywESYZJ/ZUVoVVLl0xKms+hRBCCCGEEELcD9IRsYUmFnxGa96y24I4oRml7O8vUXZN/DhlYt7n4lSLlcMTCnBMAz/R9JfzjopawWamFfDjKwuEie6FWvpximkoyp6Fn2Qs+DGmaVD1bE6OVbFNk+GKvez4SwslQO/rWp0TQgghhBBCCCHE3ZBCxBYa6ytQ92OiJOWd6TYzzYCpRkCYZAyUoOiY9Jdc5pohnXh1hkOcQZpp+jyLA/35sQAuTrUAsAxFojXmYieFH6d4lklfwcKzLT5xaKA3ElL3Yz775N5lx1+rUCJrPoUQQgghhBBCbCYZzdhCp0+OcGW2zQ8uzTHfDplrRYRJhgLiJOXdG01+fGWOWT/pPcZUy1d3Fh2TTx8bpOjafOGpQ9QKNlPNkKpn8fF9fSiliNOMNMvohCl1P6bgmDwyWqFWsJmsB9QK+WNXdjksXTHa1QwSWfMphBBCCCGEEGLTSEfEFjo+WmNPzWOuHTGx4OPaJo5t0AxiGmFCssYii1TfLEQYCgYrHq5lMVyxOT5a6xUT6n5MrWCjFPzoyjxhorEM2F11sE2TKNW3DJ48fXKEF1+9DLBh54QQQgghhBBCCHG3pCNii4Wp5qljQwxVXA4MFBmueDim0StCqA0em4dWZtT9mNMnR3q3nz45Qt2Pqfsxjx3o52B/iVrRZmxXgT27Spw60s++/iIvn53a8Ny6K0Zv1TkhhBBCCCGEEELcLemI2GLdnIiqZxPEKSXXoq/oUA/yHIbFTZ3oJY9Z+n2cZsuKA911m60w5s3355lpR3SilL6CzfHdVY7trgKQaX1bWQ9LuyyEEEIIIYQQQojNJh0RW6zbvbC76hLEKTcaAfPtGIPuVgyFWqMtomDBQ8OlZeMb/397dx4k93nfd/797WO6G3P04BwMByQBiqQECaEuUhItmVFk0YbNlOXackLvoXATbXEPR2UrVm2U/cfl7G4VU5uYKTvHLjZOhUkcO/JVcZkRFEqWCxuFuiVSkEYkIUAiORwOLk7Pge6ePp79o3tGAwKgQAL49WDwflVNdffT3dNPYx6APR8+z/e7vt1mPmBusUnqQrVcoNVJfPH7Z3j25QXAWg+SJEmSpI3BICJjq8cf9u4Y4ZbtW2h2uoxVCtw0XiYX0Ookuuu2QOSAciFHIZ8nCLZuKa4dsVjfbvPbMwuUC3kqQ3lyuSCfC3IRPPVijR+cXuJLx8/wnZdqPPrEs0zP1gbz5iVJkiRJNzyDiAHYP1nlzolhavU2ryy3mK+36HS7bB8unncMY1WQaLa7vLzQ4KZqae2Ixcx8ndFy73TNUrNNMd8LILoJbhovUynmeOVci2deXuLOXSPsnxyjVm9x6MgJwwhJkiRJ0kBYI2IAHn96hkc+83c/zYIAACAASURBVAyFfNDtdul04Uy7S7mQo1QIivkcjZUO7QRdoN5OVIo5JqtlXqo1+eDuXg2H1XoT1UqRkVKBZqtLPgdDhRxbhgpUtyQiF7zvTdupVooAa5eHj85ddi2I1ToUM/N1psYrP7b7hiRJkiRJl+KOiAF47MnnGS4VWGl3GS4VyOd6dSHOtboUcsFys0Mnnf/DyUev4OQr537UMWN9t4y/NDXGYqPFqcUmJ2sNnn5xnlOLTW4er6ztmlg1Wi5cVuFKOL8OxWS1fM12VEzP9o6NfPIPnvL4iCRJkiRtYgYRAzC30GC0lKfZ7lIu5hkpFSjkgm5KJKCQ631FQL5/u92FhXqbD9y+fW03wvp2m/PnWkSkteMZACl12TY8xGKjfd7rv57ClevrUOQi1q7/uFagr0dWYYckSZIkafA8mjEAE2NlFuotSoUc7U5iqJAjpQK56LC80lkrVhn0woihfJAPKBXzfPTeW8/7XqvtNv/6/3OGvTtG145eANTqLV58pU55qPdjHi0XWGy0qdVbPHjPnsua68x8nclq+byx17Oj4nKsDzvgjR0fkSRJkiRdH9wRMQAP3XsLy802Q4UcrU6H5WaH5ZU29WbnvI4ZCYgEzVbiXKvL0Gv8tFZ3WaxabrZZqK9w/MwyW4o5VtodZmsNqpUiD9+377J/wZ8ar1zRjorLsb7o5qqrHXZIkiRJkjYGg4gBeOCuKT71s29mZKjASifRbHdotrpELhgrF877oXSAQj4YHsqTy+UueWRhYqzMYrMD9EKI2VqDcytdxsoFioU89VaXj31gL5+4/87XtctgfR2Kbkpr11frVFwNWYQdkiRJkqSNwSBiQG7bOcLtE6P84rtv5n/4ydsAaHUTS832BS08x7cUuWXbFgr53CXrM6zusqjVW5xdbtLpJrop8c6bx6+orsP6OhRvZEfF5cgi7JAkSZIkbQzWiMjY40/P8NiTz/PMywuUC3nefes4c7Vem06A9KoUIh+wb8cIjVaHUjF/ySMLD9w1BfQ6crzwyjnGygXeefM4d0yMAVd21GG1DsW1shp2rG8R+uA9e6wPIUmSJEmbkEFEhh5/eoZHPvMMw6UChQja3S7/+dgZ8gHFHLT6aUQ+oN0PJIaH8jRaHZrtLm+7aew1jyw8cNcUD9w1xaNPPEut3jqvcOVGP+pwrcMOSZIkSdLG4NGMDD325PMMlwq9Vpi5oNHq0mh1mK+32TKUp9hvu9ml1zEDesc1Xq412Lu9wlAhf1lHFjzqIEmSJEnaqAwiMrTa2WK52Wal06XdTeSj1x3j3EqHUiHHzpGhXoHJfLBtS5GPvOMmdlfLfHd2iZMLdSrFHL/zn3/Ao088e9GilZBNXQdJkiRJkt4Ij2ZkaGKszEK9xUKjRbmQZyifY6nZpphLQBBAPhecq3dICfbvHqV2rsWppSbz51p84ZkG7927lb9081Zq9RaHjpy4ZMDgUQdJkiRJ0kbkjogMrXa2WGq0yUUiJSjmc9z/1gnu2TvOSjex1OyQzwW7qyWOn17myHOnWGl1CRLtTpdvvFDj+ycXr6gThiRJkiRJg2IQkaEH7priUz/7ZkbKBZaaXUrFHB+4fTt3TIyxvNLlLbtH+Vsf2Mdde8bZNlyi1mjR6iSa7Q71VheiVzviqRd7RzKupBOGJEmSJEmD4NGMjD1w1xS37Rzh0JETVCtFRssFavUWr5xr8f7btwFw+65hvv7DeVbaiW43kc8lAshF0Gx36NR7LTU2eicMSZIkSZJezR0RA3CxYpI/eft2SoVeLrRjpMy7bx0HEgko5IKxSpFcBN1+W087YUiSJEmSrkfuiBiQVxeTfPzpGX7789+n3U1sGy6ye6zMWKVAs9WlVMhTzAfdbqLe7VAu5KhWijx4zx4LUkqSJEmSrisGEQM0PVvj8NE5vnz8NM+dXKaQg1wuaLbbLDTavHfvduorbZ47tcxSs81IucA7b67y9lu28Yn77xz09CVJkiRJet0MIjK2Gj58d7bGC2fq7B4b4rmTS3S6AMH2SpFcLnjzxAgjpQLnhgrcv3OU0XKBxUbb4xiSJEmSpOuaNSIyND1b49CRE736DudaEPDtlxZpdRJbhnIUcsHSSptSIcdsrUGzky6oJfHwffs8jiFJkiRJum65IyJDh4/OUa0UWWl3OH56GYDlZpsI6HRz5HM5VtpdSoUcZ5ZXuPdNOy6oJSFJkiRJ0vXMICJDM/N1Cjn41gs18gEpQS4HK60ujXaimOtSLAQLjTbFfI47J4Z59IlnmZmvMzVe4c6JYZ6dW167ffDAhCGFJEmSJOm64tGMDE2NV5ieXaRUyLFrrEwnwVA+Ry6gvtJivr7CmaUVfnhmmaVGi7//Z9P8xTMnKebhB6eXeOQzz3Di1BKT1TK1eotDR04wPVsb9NuSJEmSJOmyuSMiQwcPTPAn35xhvFKgUsyzY2SIuYUGuVzQTVAqBIVcjlany2KjTURwbqXNN5+vUcwFw6UCLy822bdzhGqlCPSOe1xsV8RqUUx3T0iSJEmSNhJ3RGRo/2SVn7x9OxHBYrPN+JYh9mzdwt4dI+wYLXH7rlGGCkEClpptVjod5s+t9IpXLjQYLeVZarTXvt9oucDMfP2C11lfFNPdE5IkSZKkjcQgImP/3b23ctvOEd67bzvv2beNeqsDCUr5HO1Ol4V6r3glQLebWKi3afd6e7LY7DBS/tEmlsVGm6nxygWvsVoUs1opkotYu3746Fwm71GSJEmSpEsxiMjY/skqD9+3j1a7w+enT1Krt+ikLiPlAqeXVijkg5SgVMiRCHK54NRSk21bhlhuttk9WqKbUq8FaL3FwQMTF7zGzHyd0fL5p24utXtCkiRJkqQsWSNiQM61urxn3zYarTZf+N5JTpxepr/xgVz0goNELylaqLeYGC2zbWSIlxebnGt1eNtNVR68Z89F6z5MjVeo1VtrdSTg0rsnJEmSJEnKkkHEAKw/OnFyoc5io00+l6ObugSQgIhg65YihVzQ7iTevXcbo+UCi4322k6ISxWfPHhggkNHTgCc95wH79mT3ZuUJEmSJOkiDCIGYGa+zmS1DMBTL9bYMlRgqJBjeaVNIZej002Uizn+8p27+NL3z/DWPb0uGaeXGhw7uczppSa//qff5Td+/q0AF+2O8fB9+84bv9TuCUmSJEmSsmQQkaHVlprffWmB5+YWedtNYyw122wp5ul0E8V8ECTOtTssr7RptTvcvL3CLduHOb3U4Os/nKdUyLFtS5GzSyv8w88+Szclbt0+fF53jIfv28f+yeplBQ+2+ZQkSZIkZclilRlZ31Lz7TePsdho8+XjZynmgnqrS73VodWBXASVYp6tW4qca3XZNVJisdHm2MllSoUc5WKelU5i28gQp5eanF1eecPdMWzzKUmSJEnKmkFERtbXhdg1WuEtu0dYarY51+pyrtmm2+1SKgTtLrS7ibtv3Uq1UiQBtXqL00tNhvJBo9Wh2e5y+85hmu0OK+3uea/zerpj2OZTkiRJkpQ1g4iMrG+peWqxwfHT59g9VuKWbRV+4vbtNNuJs0srnFtps2u0xNbhIUbLBVY6iYfv28f2kRKvnGtTKuZ51y3j7BwtUyrkGSqc/yNcbLQp5YNHn3iWT/7BUzz6xLOX3OFgm09JkiRJUtasEZGR9S01j53qHbMAqFaGmNpaYbhUIJ8L3rRzmGa7y385doZiPkc+Hxw+OsdD997C56ZP0e12OXZyiS+fOEunm9g1WqJWb611x3jh7Dm6KVEs5C9aN+JSc1plm09JkiRJ0rXkjoiMHDwwQa3eolZvsVhvkVLqHbHYNcyxk8vsHBmi3e3SbHfpdLucXlrh5FKTt++pUqu3+Nz0Kd6ye5hnXl7i7HKL7cND3DVVZbhU4GStzuem5/jKibPMLTQYLRUu67jF+jl1U1q7fvDAxAD+hCRJkiRJNwKDiIysttSsVooQEBG8+9ZxdoyUWWi02DKU5+atWygV87y80KQylGNrpcjEWIVqpUin0+WxJ5+n3e0Vqrx91wj7do4wWipw/Mw53rtvOz+1fxf1Vodn55Y4tdhYe+1LHbdYP6fZWoNqpXjRnROSJEmSJF0tHs3I0GpLzYMHJjh05ATFfJ5uSgzlcyw12rz3tm3sHC2z2GgxlAtKQz+qKfHs3BKL9RZT1TLNVodvPD/Pu24Z5+WFBq1Od+14xY6R3lGNY6eW2TlaBl77uMXltvmUJEmSJOlqcEfEAKzuRDi5UOf3vvI83z+1xKmlJi+9cu5HwUSzw+07hwE4dmoZAkYrRVY6iXIxT6mQ49ip5bVjGqtu3zVMSr3Clx63kCRJkiRtNO6IGJDjp5b49swCO0ZK5Ei8VGvwxe+f5blTy7x9qkp9uMtQobdj4uzSCvkcvGNPleOnzwEwlA/OLq1QyAWT1fLa990xUuYtu0eZXWgyW2swNV7hwXv2uOtBkiRJkrQhGEQMyGNPPs9wqUAhF8zWmpQKOXIRtDpdmp1EuZDjyyfOEASVYo7bdg6zd8cIW4eHOHZymdNLTbaPlNa6aazvnJHL5fiNn3+r4YMkSZIkacMxiBiQuYUGu0aGeKnWoJCDboKVdpeFRpev//AVdo2W+JkDu1lstPnhmWWWmh1q9Rbbhkvsn8xTq7fWCkvetnOEw0fnmJmvuwNCkiRJkrShGUQMyMRYmYV6i2a7CymxvNKhmyAikQuYW2xwZqnJztEyt24fZqXdoVopXjRssOCkJEmSJOl6YRCRocefnuGxJ59nbqFBMRecXW4SkaPeDyFSSpQLeQr5oEhurfPFaLnAbK3NJ+6/c9BvQZIkSZKkK3LNumZExM0R8YWImI6I70TEr/THt0XEExHxXP9ya388IuK3IuJYRDwdEe+6VnMbhMefnuGRzzzDQr3FrpEhCvkcETkqhaDZ6ZLPwc1bK2wZyrPSSewYGWKp0QZeu/2mJEmSJEnXk2u5I6IN/FpK6RsRMQp8PSKeAP574PMppUci4lPAp4C/C/wscEf/673AP+9fbgrri1PO1BqstLsEMD5c4r437+I7Ly3Q6iS2lAos1lt0EoyV82vtNx+8Z88Vz2F6tnZeLYmDByY80iFJkiRJytQ12xGRUppNKX2jf30RmAamgI8Aj/Uf9hjwC/3rHwH+der5EjAeEZPXan5Zm1tokIvEbK1Bp5MIYHmlxfdeXuSV5RV2jJR4z75tfPDNu3j7zeMAjFWKVCvFtaKUV2J6tsahIyeo1VtMVsvU6i0OHTnB9GztKrw7SZIkSZIuTyY1IiJiL/BO4MvAREppFnphRUTs6j9sCnhh3dNe7I/Nvup7PQw8DHDLLbdc03lfTRNjZY6fXKKQCxKJpWabTicB8KXjZ6luKVDKB0vNInt3jPA/ffBNV3W3wuGjc1T7wQawdnn46Jy7IiRJkiRJmblmOyJWRcQI8EfAr6aUFl7roRcZSxcMpHQopXR3SununTt3Xq1pXnMP3XsL51od2t0uy802nU6XVhcqhRz5fFAuFjh+5hwf+8BePnH/nVc9HJiZrzNaPj93Gi0XmJmvX9XXkSRJkiTptVzTICIiivRCiN9NKf1xf3hu9chF//Jkf/xF4OZ1T98DvHQt55elB+6a4oN37KCQy7HSSSSC0VKeLaUCpUKOsXKBVqfL4aNz1+T1p8YrLPaLX66yCKYkSZIkKWvXsmtGAL8DTKeUfnPdXX8KPNS//hDwH9aN/41+94z3AbXVIxybxcc/fAc/cfsO3jo5xnApT3koT7sLW7cM0Wx32T48dFk7FKZnazz6xLN88g+e4tEnnr2sOg8HD0ysFb7sprR2/eCBiavx1iRJkiRJuizXckfE+4GPAh+KiG/1v34OeAS4PyKeA+7v3wb4j8Bx4Bjw/wL/yzWc20Dsn6zy8H37OHDTGClBpwu7x0rkc0Gz3WWyWv6xOxTeaNHJ1deuVorM1hpXrQimJEmSJEmvR6R0QRmG68bdd9+dvva1rw16Gm/I40/P8Nt//n1and5OiMlqmVwu92PDgUefeJZavbVWbBJYu/2J++/MYuqSJEmSJL2miPh6Sunui92XSdcMXeiBu6a4becIh4/OMTNfZ2q8wsEDEz92h8LMfJ3Javm8MYtOSpIkSZKuFwYRA7R/svq6j0ZMjVcu2BFh0UlJkiRJ0vXCIGKDmJ6tXdbuiIMHJjh05ATQ2wmx2GhTq7d48J49WU9ZkiRJkqTX7Zq279TleT0FKC06KUmSJEm6nrkjYgM4fHSOaqW4dtyi1elw/NQSf+fTT/HTb919we6IN3KkQ5IkSZKkjcAdERvAzHyd0XIvEzq91ODrP5wnpQSJy27PKUmSJEnS9cAgYgOYGq+w2GgDcOzkMqVCjohgtL9Lolopcvjo3IBnKUmSJEnSlfNoxoCsL045lA/mFpqwbQu1+gqlfI5mJ/G2m8YA23NKkiRJkjYPd0QMwKuLUw4V8nRTotXukIscRPCuW8bZOVoGbM8pSZIkSdo8DCIGYH1xylwE1UqRW7cPs2O0zD/663dx286RtXCiVm9Rq7c4eGBi0NOWJEmSJOmKGUQMwPrilKtWj1/YnlOSJEmStJlZI2IApsYr1OqttXadcP7xi6vZnnN9LYqp8coFrUAlSZIkScqSQUSGpmdr/Nsnf8iTx89wemmFibES7751K+VigVq9xYP37Lnoc95okLBai6JaKTJZLa+1AnWHhSRJkiRpUDyakZHp2Rr/8LPP8uTxs4yUCuweK3FqaYUvPHOKlXaHD+/fyeGjc3zyD57i0SeeZXq2dkFRy9UgYXq2dlmvebFaFLYClSRJkiQNkjsiMnL46Bynl5qMlguUi3kqQwUqQ70//gA+N33qgp0LW4q5tfAAWLs8fHTusnY0zMzXmayWzxuzFagkSZIkaZDcEZGRmfk6zXaHUuFHf+SlQo6VdpdvvlC76M6Fb75Qu2RRy8sxNV5hsdE+b8xWoJIkSZKkQTKIyMjUeIVSIU+z3V0ba7a7DBVyJNJFA4dEuqIg4eCBibX2n7YClSRJkiRtBAYRGTl4YIIdIyUWG23qK20aK22WGm22DQ/xzpvHzwscTi02OPLsKZqtDl/6/hlOnFp6Q0GCrUAlSZIkSRuNNSIysn+yyid/5k7+7ZM/5Jsv1Egk3jwxwvjwEKeWmrxwps6dEyNsKeX56olXSMB7b9vGuWaHZ+aWONfq8Labqjx4z57XFSRczVagkiRJkiRdKYOIDO2frPJ//ld3AT9qrTlUyPOW3WNsKeZ5Zm6JZrvDWLnIgakxdoyUYRS2DpeoVop84v47B/wOJEmSJEm6MgYRA7K+tSbA3h0jbB0u8ZUTZ7nvzp3kItYea6cLSZIkSdJmYY2IAZmZr1+TApWSJEmSJG1kBhEDcqnWmu+8edxOF5IkSZKkTcsgYkAu1Vrzo/feaqcLSZIkSdKmZY2IAVltrXn46Bwz83WmxivndcQweJAkSZIkbUYGEQNka01JkiRJ0o3GoxmSJEmSJCkzBhGSJEmSJCkzBhGSJEmSJCkz1ojYwKZna+cVszx4YMKaEpIkSZKk65pBRIZeT7AwPVvj0JETVCtFJqtlavUWh46csJWnJEmSJOm65tGMjKwGC7V667xgYXq2dtHHHz46R7VSpFopkotYu3746FzGM5ckSZIk6eoxiMjIq4OFlXaH46eW+LVPP82jTzx7QSAxM19ntHz+hpXRcoGZ+XqW05YkSZIk6aoyiMjI+mDh1GKDbzw/DynRTd2L7o6YGq+w2Gif9z0WG22mxiuZzluSJEmSpKvJICIj64OFY6eWKRVyEEG1MnTRYxcHD0xQq7eo1Vt0U1q7fvDAxKDegiRJkiRJV8wgIiPrg4XFeouUEs12l9t3DQMXHrvYP1nl4fv2Ua0Uma01qFaKFqqUJEmSJF337JqRkdVg4fDROQiICN59a5UdI2Xg4scu9k9WDR4kSZIkSZuKQUSGVoOFgwcmOHTkBIv1Ns/NnebscotCLvj4T71p0FOUJEmSJOmaMojI0PRsjcNH55iZr3OyVuc7s4t0ul1GygXePDHK56ZPcdvOEXdBSJIkSZI2LWtEZGR6tsahIyeo1VsUcvRDiMSt27ewa7TMywtNut3ueQUrJUmSJEnabAwiMnL46Nxad4zjp88RAZVijlfOtSgX85QKOWZrjfMKVkqSJEmStNkYRGRkZr7OaLl3Emap0aZSzENAs90FoFTIcWZ55YKClZIkSZIkbSYGERmZGq+w2GgDMFIuMFzKs9JJ5ANSSiw02hTzOQ4emBjwTCVJkiRJunYMIjJy8MAEtXqLWr3FbTu20O3CcDHPztESZ5dbkODjH3qThSolSZIkSZuaXTMysn+yysP37ePw0TmWmm3ee9s2Amh2ElPjFQ4emFgLIdZ313j1fZIkSZIkXc8MIjK0f7L6YwOF1e4a1UqRyWqZWr3FoSMnePi+fZmGEYYhkiRJkqRrwSAiQ5fzy/367hrA2uXho3OZBQEbJQyRJEmSJG0+1ojIyOov97V667xf7qdna+c9bn13jVWj5UKmbT3XhyG5iLXrh4/OZTYHSZIkSdLmZBCRkcv95X59d41Vi412pm09N0IYIkmSJEnanAwiMnK5v9yv767RTWntepZtPTdCGCJJkiRJ2pwMIjJyub/cr3bXqFaKzNYaVCvFzGszbIQwRJIkSZK0OVmsMiMHD0xw6MgJoLcTYrHRplZv8eA9ey547OV017iW1rcaXS2s+eA9eyxUKUmSJEm6YgYRGbnefrkfdBgiSZIkSdqcDCIy5C/3kiRJkqQbnTUiJEmSJElSZgwiJEmSJElSZjyacQOYnq1x+Ogc33mpxkKjTbVS4K2TVQ4emPCoiCRJkiQpU+6I2OSmZ2scOnKCE6eWePFsnYV6i+fPnOMHp5c4dOQE07O1QU9RkiRJknQDMYjY5A4fnaNaKfLyYpNSMUe1UqRczPPyQpNqpcjho3ODnqIkSZIk6QZiELHJzczXGS0XWGq0KRV6P+5SIcdCo8VoucDMfH3AM5QkSZIk3UgMIja5qfEKi402I+UCzXYXgGa7y1i5yGKjzdR4ZcAzlCRJkiTdSAwiNrmDByao1VvsHi3RbHWp1Vs0Wh12j5Wo1VscPDAx6ClKkiRJkm4gds3Y5PZPVnn4vn0cPjrHuVZnrWvG3h0jds2QJEmSJGXOIOIGsH+yauAgSZIkSdoQPJohSZIkSZIy446IDD3+9AyPPfk8cwsNJsbKPHTvLTxw19SgpyVJkiRJUmYMIjLy+NMzPPKZZxguFdg1MsRCvcUjn3kGwDBCkiRJknTD8GhGRh578nmGSwWqlSK5XI5qpchwqcBjTz4/6KlJkiRJkpQZg4iMzC00GC3lzxsbLeWZW2gMaEaSJEmSJGXPICIjE2NlFpud88YWmx0mxsoDmpEkSZIkSdkziMjIQ/fewnKzTa3eotvtUqu3WG62eejeWwY9NUmSJEmSMmOxyoysFqRc3zXj4x96k4UqJUmSJEk3FIOIDD1w15TBgyRJkiTphubRDEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlBmDCEmSJEmSlJnCoCdwI5merXH46Bwz83WmxiscPDDB/snqoKclSZIkSVJm3BGRkenZGoeOnKBWbzFZLVOrtzh05ATTs7VBT02SJEmSpMwYRGTk8NE5qpUi1UqRXMTa9cNH5wY9NUmSJEmSMmMQkZGZ+Tqj5fNPwoyWC8zM1wc0I0mSJEmSsmcQkZGp8QqLjfZ5Y4uNNlPjlQHNSJIkSZKk7BlEZOTggQlq9Ra1eotuSmvXDx6YGPTUJEmSJEnKjEFERvZPVnn4vn1UK0Vmaw2qlSIP37fPrhmSJEmSpBuK7TsztH+yavAgSZIkSbqhuSNCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlxiBCkiRJkiRlJlJKg57DGxYRp4AfDngaO4DTA56DdK25znUjcJ3rRuA612bnGteN4HpZ57emlHZe7I7rOojYCCLiaymluwc9D+lacp3rRuA6143Ada7NzjWuG8FmWOcezZAkSZIkSZkxiJAkSZIkSZkxiLhyhwY9ASkDrnPdCFznuhG4zrXZucZ1I7ju17k1IiRJkiRJUmbcESFJkiRJkjJjECFJkiRJkjJjEHEFIuJgRDwTEcci4lODno/0ekTEv4yIkxFxdN3Ytoh4IiKe619u7Y9HRPxWf60/HRHvWvech/qPfy4iHhrEe5EuJiJujogvRMR0RHwnIn6lP+4616YREeWI+EpEPNVf57/RH98XEV/ur9l/HxFD/fFS//ax/v17132vv9cffyYifmYw70i6uIjIR8Q3I+LP+rdd49pUIuIHEfHtiPhWRHytP7ZpP7MYRLxBEZEH/inws8Bbgf86It462FlJr8u/Ag6+auxTwOdTSncAn+/fht46v6P/9TDwz6H3jyPw68B7gfcAv776D6S0AbSBX0sp7QfeB/xy/99p17k2kybwoZTS24F3AAcj4n3APwAe7a/zV4CP9R//MeCVlNLtwKP9x9H/u/FLwNvo/bfhn/U/60gbxa8A0+tuu8a1Gf2VlNI7Ukp3929v2s8sBhFv3HuAYyml4ymlFeD3gY8MeE7SZUspHQHOvmr4I8Bj/euPAb+wbvxfp54vAeMRMQn81STSeAAABdtJREFUDPBESulsSukV4AkuDDekgUgpzaaUvtG/vkjvA+wUrnNtIv31utS/Wex/JeBDwB/2x1+9zlfX/x8CPxUR0R///ZRSM6V0AjhG77OONHARsQd4APgX/duBa1w3hk37mcUg4o2bAl5Yd/vF/ph0PZtIKc1C75c4YFd//FLr3b8Hui70t+a+E/gyrnNtMv0t698CTtL70Pl9YD6l1O4/ZP2aXVvP/ftrwHZc59rY/jHwvwLd/u3tuMa1+STgP0XE1yPi4f7Ypv3MUhj0BK5jcZExe6Fqs7rUevfvgTa8iBgB/gj41ZTSQu9/jF38oRcZc51rw0spdYB3RMQ48CfA/os9rH/pOtd1JSL+KnAypfT1iPjg6vBFHuoa1/Xu/SmllyJiF/BERHzvNR573a9zd0S8cS8CN6+7vQd4aUBzka6Wuf62LvqXJ/vjl1rv/j3QhhYRRXohxO+mlP64P+w616aUUpoH/oJeTZTxiFj9H07r1+zaeu7fX6V3TM91ro3q/cDPR8QP6B2F/hC9HRKucW0qKaWX+pcn6YXK72ETf2YxiHjjvgrc0a/YO0Sv+M2fDnhO0pX6U2C1uu5DwH9YN/43+hV63wfU+tvDPgv8dERs7RfC+en+mDRw/TPBvwNMp5R+c91drnNtGhGxs78TgoioAB+mVw/lC8Av9h/26nW+uv5/EfjzlFLqj/9Sv+PAPnoF0L6SzbuQLi2l9PdSSntSSnvpfd7+85TSf4trXJtIRAxHxOjqdXqfNY6yiT+zeDTjDUoptSPib9P7weaBf5lS+s6ApyVdtoj4PeCDwI6IeJFehd1HgE9HxMeA54G/1n/4fwR+jl5hp3PA3wRIKZ2NiP+dXjAH8PdTSq8ugCkNyvuBjwLf7p+fB/jfcJ1rc5kEHutX/88Bn04p/VlEfBf4/Yj4P4Bv0gvl6F/+m4g4Ru//Ev8SQErpOxHxaeC79DrO/HL/yIe0Uf1dXOPaPCaAP+kfHy0A/y6ldDgivsom/cwSvYBQkiRJkiTp2vNohiRJkiRJyoxBhCRJkiRJyoxBhCRJkiRJyoxBhCRJkiRJyoxBhCRJkiRJyoxBhCRJet0iohMR34qI70TEUxHxdyIi17/v7oj4rdd47t6I+G+ym60kSdpIbN8pSZJet4hYSimN9K/vAv4d8MWU0q9fxnM/CHwypfRXr+0sJUnSRuSOCEmSdEVSSieBh4G/HT0fjIg/A4iIv9zfOfGtiPhmRIwCjwA/2R/7RH+HxP8XEd/of/1E/7kfjIi/iIg/jIjvRcTvRkT077snIv5LfzfGVyJiNCLyEfF/RcRXI+LpiPgfB/VnIkmSLq0w6AlIkqTrX0rpeP9oxq5X3fVJ4JdTSl+MiBGgAXyKdTsiImILcH9KqRERdwC/B9zdf/47gbcBLwFfBN4fEV8B/j3wYErpqxExBtSBjwG1lNI9EVECvhgR/ymldOJavndJkvT6GERIkqSrJS4y9kXgNyPid4E/Tim92N/UsF4R+CcR8Q6gA9y57r6vpJReBIiIbwF7gRowm1L6KkBKaaF//08Dd0XEL/afWwXuAAwiJEnaQAwiJEnSFYuI2+iFCCeB/avjKaVHIuJx4OeAL0XEhy/y9E8Ac8Db6R0bbay7r7nueofeZ5cALlbkKoCPp5Q+ewVvRZIkXWPWiJAkSVckInYC/zfwT9KrqmBHxJtSSt9OKf0D4GvAW4BFYHTdw6r0djh0gY8C+R/zkt8DboqIe/qvMRoRBeCzwP8cEcX++J0RMXzl71CSJF1N7oiQJElvRKV/VKIItIF/A/zmRR73qxHxV+jtZvgu8BmgC7Qj4ingXwH/DPijiPhrwBeA5dd64ZTSSkQ8CPx2RFTo1Yf4MPAv6B3d+Ea/qOUp4Beu8H1KkqSrzPadkiRJkiQpMx7NkCRJkiRJmTGIkCRJkiRJmTGIkCRJkiRJmTGIkCRJkiRJmTGIkCRJkiRJmTGIkCRJkiRJmTGIkCRJkiRJmfn/AV/7K0KkLU3MAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (18,12)\n",
    "\n",
    "plt.scatter(\n",
    "    distance, \n",
    "    speed, \n",
    "    alpha=0.5\n",
    ")\n",
    "plt.title(\"Distance x Speed\")\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"Speed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting Our Scatterplot\n",
    "\n",
    "We can see pretty clearly that as distance increases, average speed across that distance increases rapidly and then levels off as the distance increases.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Query the `on_time_dataframe` to focus on two numeric fields. \n",
    "2. Plot a histogram of one of these fields\n",
    "3. Plot a scatterplot of both of these fields\n",
    "\n",
    "# Predicting Speed Given Distance\n",
    "\n",
    "It is often the case that once we characterize a distribution, we want to create a function to predict one variable given the other. Lets take this example further by fitting a polynomial regression to describe our data. We use `sklearn.pipeline.Pipeline` to chain a `sklearn.preprocessing.PolynomialFeatures` to a `sklearn.linear_model.LinearRegression`. Other than that, we simply define x and y, and fit a model to those values. Then we finally compute a cross value score, to see the model's performance. We'll see this pattern again when we use large data tools in Spark MLlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vagrant/anaconda/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.62454561, 0.70210257, 0.6544795 ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "\n",
    "x = np.array(distance)\n",
    "y = np.array(speed)\n",
    "x_test = np.arange(0, 5000, 100)\n",
    "\n",
    "model = Pipeline([\n",
    "    ('poly', PolynomialFeatures(degree=3)),\n",
    "    ('linear', LinearRegression(fit_intercept=False))\n",
    "])\n",
    "model = model.fit(x[:, np.newaxis], y)\n",
    "model.named_steps['linear'].coef_\n",
    "y_out = model.predict(x_test.reshape(-1,1))\n",
    "\n",
    "cross_val_score(model, x.reshape(-1,1), y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Polynomial Fit\n",
    "\n",
    "Because we are running a polynomial regression, where we get to decide the degree of the polynomial. To help decide, lets plot a polynomial fit line to the data using `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (18,12)\n",
    "\n",
    "plt.scatter(\n",
    "    distance, \n",
    "    speed, \n",
    "    alpha=0.5\n",
    ")\n",
    "plt.plot(\n",
    "    x_test,\n",
    "    y_out, \n",
    "    color='orange', \n",
    "    linewidth=3\n",
    ")\n",
    "\n",
    "plt.title(\"Distance x Speed\")\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"Speed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Joining Data in PySpark\n",
    "\n",
    "Next we're going to learn how to join between datasets using PySpark. We're going to pick up from an example we're going to work in chapter 6, and explore it more deeply. To begin with, we will prepare a list of `TailNum` (tail numbers) from the FAA flight records. These uniquely identify each airplane from each flight.\n",
    "\n",
    "### Unique Tail Numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+\n",
      "|TailNum|\n",
      "+-------+\n",
      "| N396AA|\n",
      "| N3CWAA|\n",
      "| N499AA|\n",
      "| N4YUAA|\n",
      "| N567AA|\n",
      "| N442AS|\n",
      "+-------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tail_numbers = on_time_dataframe.select(\"TailNum\").distinct()\n",
    "tail_numbers.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FAA Airplane Records\n",
    "\n",
    "We will trim the FAA records down to just the `TailNum`, `Model` and `Engine_Model`. Note that we go ahead and rename the `TailNum` field to `FAATailNum` using the `pyspark.sql.functions.alias()` method. This avoids having two fields referenced by the same name once we perform our joins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----------+------------+\n",
      "|FAATailNum|      Model|Engine_Model|\n",
      "+----------+-----------+------------+\n",
      "|    N933EV|CL-600-2B19| CF34 SERIES|\n",
      "|    N917WN|    737-7H4|  CFM56-7B24|\n",
      "|    N438WN|    737-7H4|CFM56 SERIES|\n",
      "|    N283VA|   A320-214| CFM56-5B4/3|\n",
      "|    N473UA|   A320-232| AL-25SERIES|\n",
      "|    N637JB|   A320-232|    V2527-A5|\n",
      "+----------+-----------+------------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "faa_tail_number_inquiry = spark.read.json('../data/faa_tail_number_inquiry.jsonl')\n",
    "airplane_records = faa_tail_number_inquiry.select(\n",
    "    faa_tail_number_inquiry.TailNum.alias(\"FAATailNum\"), \n",
    "    \"Model\",\n",
    "    \"Engine_Model\"\n",
    ")\n",
    "airplane_records.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Joins\n",
    "\n",
    "You may be familiar with an inner join from SQL. An inner join joins two datasets based on the presence of a key from one dataset in the other. Records which don't have a key that appears in the other table do not appear in the final output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAA tail numbers: 4,898\n",
      "Airplane records: 3,988\n",
      "Joined records:   3,988\n"
     ]
    }
   ],
   "source": [
    "# INNER JOIN\n",
    "print(\n",
    "    \"FAA tail numbers: {:,}\".format(\n",
    "        tail_numbers.count()\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Airplane records: {:,}\".format(\n",
    "        airplane_records.count()\n",
    "    )\n",
    ")\n",
    "\n",
    "inner_joined = tail_numbers.join(\n",
    "    airplane_records, \n",
    "    tail_numbers.TailNum == airplane_records.FAATailNum, \n",
    "    'inner'\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Joined records:   {:,}\".format(\n",
    "        inner_joined.count()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inner Join Results\n",
    "\n",
    "Note that there are as many records in the output as there were in the FAA Airplane dataset - indicating that there was a representative of every tail number from that dataset in the on-time performance flight records. Lets take a look at the records themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+--------------+------------+\n",
      "|TailNum|FAATailNum|         Model|Engine_Model|\n",
      "+-------+----------+--------------+------------+\n",
      "| N396AA|    N396AA|          B300|    PT6A SER|\n",
      "| N499AA|    N499AA|DC-9-82(MD-82)| JT8D SERIES|\n",
      "| N442AS|    N442AS|     737-990ER| CFM56-7B27E|\n",
      "| N369NB|    N369NB|      A319-114|CFM56 SERIES|\n",
      "| N388DA|    N388DA|       737-832|CFM56 SERIES|\n",
      "|  N6700|     N6700|       757-232|      PW2037|\n",
      "+-------+----------+--------------+------------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "inner_joined.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how convenient it is that we renamed one of the keys `FAATailNum`. If we hadn't, we'd have two columns with the same name now and would have trouble referring to one or the other."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Outer Join\n",
    "\n",
    "Another type of join is the left outer join. It ensures that one record will remain in the output from the left side of the join no matter what. If a match on the join keys is found, the fields for the record on the right will be filled. If a match is not found, they will be empty. \n",
    "\n",
    "Lets look at how this works with our two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FAA tail numbers: 4,898\n",
      "Airplane records: 3,988\n",
      "Joined records:   4,898\n"
     ]
    }
   ],
   "source": [
    "# INNER JOIN\n",
    "print(\n",
    "    \"FAA tail numbers: {:,}\".format(\n",
    "        tail_numbers.count()\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Airplane records: {:,}\".format(\n",
    "        airplane_records.count()\n",
    "    )\n",
    ")\n",
    "\n",
    "left_outer_joined = tail_numbers.join(\n",
    "    airplane_records, \n",
    "    tail_numbers.TailNum == airplane_records.FAATailNum, \n",
    "    'left_outer'\n",
    ")\n",
    "\n",
    "print(\n",
    "    \"Joined records:   {:,}\".format(\n",
    "        left_outer_joined.count()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Outer Join Result\n",
    "\n",
    "Note that there were 4,898 records on the left side of our join and there are the same number on the output of our join. Lets take a look at what both matched and unmatched records look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+----------+--------------+------------+\n",
      "|TailNum|FAATailNum|         Model|Engine_Model|\n",
      "+-------+----------+--------------+------------+\n",
      "| N396AA|    N396AA|          B300|    PT6A SER|\n",
      "| N3CWAA|      null|          null|        null|\n",
      "| N499AA|    N499AA|DC-9-82(MD-82)| JT8D SERIES|\n",
      "| N4YUAA|      null|          null|        null|\n",
      "| N567AA|      null|          null|        null|\n",
      "| N442AS|    N442AS|     737-990ER| CFM56-7B27E|\n",
      "+-------+----------+--------------+------------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "left_outer_joined.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that some records have fields filled out, and some don't.\n",
    "\n",
    "## Right Outer Join\n",
    "\n",
    "Another type of join is the right outer join. This works the opposite of a left outer join. In this case, the output will preserve a record for each and every record on the right side of the join. Use the `right_outer` join type to perform this kind of join.\n",
    "\n",
    "## Exercises\n",
    "\n",
    "1. Go back and perform a right outer join on the preceding two datasets. Is the `distinct()` call on the FAA on-time performance records still needed? Why or why not?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using `RDDs` and Map/Reduce to Prepare a Complex Record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'TailNum': 'N382DA',\n",
       " 'Flights': [('DL', '2015-01-02', '1664', 'MSP', 'DCA'),\n",
       "  ('DL', '2015-01-02', '1968', 'LAX', 'MSP'),\n",
       "  ('DL', '2015-01-03', '1303', 'SLC', 'BOS'),\n",
       "  ('DL', '2015-01-03', '1374', 'OAK', 'SLC'),\n",
       "  ('DL', '2015-01-03', '1374', 'SLC', 'OAK'),\n",
       "  ('DL', '2015-01-03', '1944', 'DCA', 'SLC'),\n",
       "  ('DL', '2015-01-03', '2071', 'SLC', 'PHX'),\n",
       "  ('DL', '2015-01-03', '966', 'PHX', 'SLC'),\n",
       "  ('DL', '2015-01-04', '1207', 'DTW', 'SLC'),\n",
       "  ('DL', '2015-01-04', '2079', 'BOS', 'DTW'),\n",
       "  ('DL', '2015-01-05', '2324', 'SLC', 'ATL'),\n",
       "  ('DL', '2015-01-05', '2352', 'ATL', 'JFK'),\n",
       "  ('DL', '2015-01-06', '332', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-01-06', '425', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-01-08', '421', 'JFK', 'ATL'),\n",
       "  ('DL', '2015-01-10', '2264', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-01-11', '2020', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-01-11', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-01-11', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-01-12', '2193', 'MSP', 'SEA'),\n",
       "  ('DL', '2015-01-12', '2424', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-01-12', '921', 'MKE', 'MSP'),\n",
       "  ('DL', '2015-01-12', '921', 'MSP', 'MKE'),\n",
       "  ('DL', '2015-01-13', '1511', 'DTW', 'DEN'),\n",
       "  ('DL', '2015-01-13', '410', 'DEN', 'JFK'),\n",
       "  ('DL', '2015-01-13', '433', 'JFK', 'DEN'),\n",
       "  ('DL', '2015-01-14', '1610', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-01-14', '2335', 'DTW', 'SLC'),\n",
       "  ('DL', '2015-01-14', '952', 'SLC', 'DFW'),\n",
       "  ('DL', '2015-01-15', '1657', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-01-15', '2056', 'DFW', 'SLC'),\n",
       "  ('DL', '2015-01-15', '466', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-01-15', '490', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-01-16', '1552', 'LAS', 'LAX'),\n",
       "  ('DL', '2015-01-16', '1577', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-01-16', '1603', 'MSY', 'MSP'),\n",
       "  ('DL', '2015-01-16', '2336', 'MSP', 'MKE'),\n",
       "  ('DL', '2015-01-17', '1071', 'MKE', 'MSP'),\n",
       "  ('DL', '2015-01-18', '2320', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-01-18', '2576', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-01-18', '42', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-01-19', '326', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-01-19', '332', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-01-19', '425', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-01-20', '2658', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-01-20', '480', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-01-22', '1728', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-01-22', '1883', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-01-22', '436', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-01-23', '2623', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-01-24', '2066', 'SLC', 'MCO'),\n",
       "  ('DL', '2015-01-24', '978', 'SAN', 'SLC'),\n",
       "  ('DL', '2015-01-25', '1574', 'MCO', 'MSP'),\n",
       "  ('DL', '2015-01-25', '2193', 'MSP', 'SEA'),\n",
       "  ('DL', '2015-01-25', '2424', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-01-26', '1511', 'DTW', 'DEN'),\n",
       "  ('DL', '2015-01-26', '2160', 'JFK', 'DEN'),\n",
       "  ('DL', '2015-01-26', '410', 'DEN', 'JFK'),\n",
       "  ('DL', '2015-01-27', '1557', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-01-27', '1610', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-01-27', '2335', 'DTW', 'SLC'),\n",
       "  ('DL', '2015-01-28', '2340', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-01-28', '582', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-01-29', '1168', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-01-29', '1325', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-01-29', '1577', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-01-29', '2531', 'BOS', 'LAX'),\n",
       "  ('DL', '2015-01-30', '1169', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-01-30', '1566', 'LAX', 'RDU'),\n",
       "  ('DL', '2015-02-01', '1097', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-02-01', '1566', 'LAX', 'RDU'),\n",
       "  ('DL', '2015-02-02', '1097', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-02-02', '1882', 'SLC', 'DTW'),\n",
       "  ('DL', '2015-02-02', '2170', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-02-03', '1542', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-02-03', '2188', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-02-03', '2436', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-02-03', '2436', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-02-06', '2658', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-02-06', '420', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-02-08', '1740', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-02-08', '2360', 'SLC', 'DCA'),\n",
       "  ('DL', '2015-02-08', '404', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-02-09', '872', 'DCA', 'ATL'),\n",
       "  ('DL', '2015-02-10', '776', 'ATL', 'SRQ'),\n",
       "  ('DL', '2015-02-11', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-02-11', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-02-11', '2561', 'SRQ', 'ATL'),\n",
       "  ('DL', '2015-02-12', '1123', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-02-12', '1830', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-02-12', '2457', 'ATL', 'BDL'),\n",
       "  ('DL', '2015-02-12', '2457', 'BDL', 'ATL'),\n",
       "  ('DL', '2015-02-14', '482', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-02-15', '1795', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-02-16', '1552', 'LAS', 'LAX'),\n",
       "  ('DL', '2015-02-16', '2215', 'MSP', 'FSD'),\n",
       "  ('DL', '2015-02-17', '896', 'FSD', 'MSP'),\n",
       "  ('DL', '2015-02-18', '1760', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-02-18', '2253', 'MSP', 'SEA'),\n",
       "  ('DL', '2015-02-19', '142', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-02-19', '1473', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-02-19', '492', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-02-20', '326', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-02-20', '436', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-02-21', '1795', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-02-21', '348', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-02-21', '409', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-02-22', '332', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-02-22', '465', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-02-25', '2048', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-02-25', '498', 'JFK', 'PDX'),\n",
       "  ('DL', '2015-02-26', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-02-26', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-02-27', '503', 'ATL', 'PDX'),\n",
       "  ('DL', '2015-02-27', '503', 'PDX', 'ATL'),\n",
       "  ('DL', '2015-02-28', '1709', 'ATL', 'BNA'),\n",
       "  ('DL', '2015-03-01', '1341', 'ATL', 'GSP'),\n",
       "  ('DL', '2015-03-01', '1341', 'GSP', 'ATL'),\n",
       "  ('DL', '2015-03-01', '2357', 'JFK', 'ATL'),\n",
       "  ('DL', '2015-03-01', '2589', 'BNA', 'ATL'),\n",
       "  ('DL', '2015-03-01', '478', 'ATL', 'JFK'),\n",
       "  ('DL', '2015-03-02', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-03-02', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-03-02', '2172', 'ATL', 'TPA'),\n",
       "  ('DL', '2015-03-02', '2172', 'TPA', 'ATL'),\n",
       "  ('DL', '2015-03-03', '1668', 'LAX', 'MSP'),\n",
       "  ('DL', '2015-03-03', '2203', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-03-03', '2215', 'MSP', 'FSD'),\n",
       "  ('DL', '2015-03-03', '2565', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-03-03', '637', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-03-04', '896', 'FSD', 'MSP'),\n",
       "  ('DL', '2015-03-05', '1287', 'DTW', 'BNA'),\n",
       "  ('DL', '2015-03-05', '1959', 'DTW', 'TPA'),\n",
       "  ('DL', '2015-03-05', '1959', 'TPA', 'DTW'),\n",
       "  ('DL', '2015-03-05', '2268', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-03-05', '2572', 'BNA', 'LAX'),\n",
       "  ('DL', '2015-03-05', '971', 'SLC', 'MCO'),\n",
       "  ('DL', '2015-03-06', '1683', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-03-06', '2540', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-03-06', '2610', 'MCO', 'SLC'),\n",
       "  ('DL', '2015-03-07', '1949', 'ATL', 'MIA'),\n",
       "  ('DL', '2015-03-08', '503', 'ATL', 'PDX'),\n",
       "  ('DL', '2015-03-08', '700', 'PDX', 'ATL'),\n",
       "  ('DL', '2015-03-08', '891', 'MIA', 'ATL'),\n",
       "  ('DL', '2015-03-09', '1755', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-03-09', '2554', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-03-10', '1979', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-03-10', '2474', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-03-10', '409', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-03-10', '42', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-03-11', '1338', 'ATL', 'BNA'),\n",
       "  ('DL', '2015-03-11', '1640', 'DTW', 'ATL'),\n",
       "  ('DL', '2015-03-12', '1324', 'BNA', 'ATL'),\n",
       "  ('DL', '2015-03-13', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-03-13', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-03-14', '1268', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-03-14', '1723', 'SLC', 'BIL'),\n",
       "  ('DL', '2015-03-14', '1740', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-03-15', '1635', 'BIL', 'SLC'),\n",
       "  ('DL', '2015-03-15', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-03-15', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-03-15', '1857', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-03-15', '2485', 'SEA', 'ATL'),\n",
       "  ('DL', '2015-03-16', '1837', 'ATL', 'AUS'),\n",
       "  ('DL', '2015-03-17', '1555', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-03-17', '1557', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-03-17', '1820', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-03-17', '1984', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-03-17', '2150', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-03-18', '1560', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-03-19', '1055', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-03-19', '2020', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-03-19', '2020', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-03-20', '1327', 'CMH', 'LAX'),\n",
       "  ('DL', '2015-03-20', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-03-21', '1325', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-03-21', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-03-22', '1659', 'BOS', 'LAX'),\n",
       "  ('DL', '2015-03-22', '729', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-03-22', '729', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-03-22', '877', 'LAX', 'IND'),\n",
       "  ('DL', '2015-03-23', '1550', 'IND', 'LAX'),\n",
       "  ('DL', '2015-03-23', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-03-24', '1325', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-03-24', '2341', 'MSP', 'OMA'),\n",
       "  ('DL', '2015-03-24', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-03-24', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-03-25', '1620', 'OMA', 'MSP'),\n",
       "  ('DL', '2015-03-25', '2595', 'DTW', 'RDU'),\n",
       "  ('DL', '2015-03-26', '1097', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-03-26', '1979', 'LAX', 'RDU'),\n",
       "  ('DL', '2015-03-27', '1055', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-03-27', '1137', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-03-27', '1545', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-03-27', '1555', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-03-27', '1820', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-03-28', '1327', 'CMH', 'LAX'),\n",
       "  ('DL', '2015-03-28', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-03-29', '1325', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-03-29', '2341', 'MSP', 'OMA'),\n",
       "  ('DL', '2015-03-29', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-03-29', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-03-30', '1620', 'OMA', 'MSP'),\n",
       "  ('DL', '2015-03-31', '1640', 'DTW', 'ATL'),\n",
       "  ('DL', '2015-04-01', '1949', 'ATL', 'MIA'),\n",
       "  ('DL', '2015-04-02', '891', 'MIA', 'ATL'),\n",
       "  ('DL', '2015-04-03', '420', 'JFK', 'DEN'),\n",
       "  ('DL', '2015-04-04', '1610', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-04-05', '1683', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-04-05', '1740', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-04-05', '1809', 'DTW', 'SLC'),\n",
       "  ('DL', '2015-04-05', '2360', 'SLC', 'DCA'),\n",
       "  ('DL', '2015-04-06', '1264', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-04-06', '2332', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-04-06', '2359', 'DCA', 'SLC'),\n",
       "  ('DL', '2015-04-06', '2378', 'SLC', 'SAN'),\n",
       "  ('DL', '2015-04-06', '637', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-04-06', '89', 'SAN', 'SLC'),\n",
       "  ('DL', '2015-04-07', '2643', 'LAX', 'LAS'),\n",
       "  ('DL', '2015-04-07', '498', 'JFK', 'ATL'),\n",
       "  ('DL', '2015-04-08', '2374', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-04-08', '889', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-04-09', '1736', 'ATL', 'CMH'),\n",
       "  ('DL', '2015-04-09', '2250', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-04-10', '1783', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-04-10', '2214', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-04-10', '2419', 'CMH', 'ATL'),\n",
       "  ('DL', '2015-04-10', '983', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-04-11', '2559', 'ATL', 'PHX'),\n",
       "  ('DL', '2015-04-11', '779', 'PHX', 'ATL'),\n",
       "  ('DL', '2015-04-12', '796', 'ATL', 'IND'),\n",
       "  ('DL', '2015-04-12', '796', 'IND', 'ATL'),\n",
       "  ('DL', '2015-04-12', '881', 'ATL', 'AUS'),\n",
       "  ('DL', '2015-04-12', '881', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-04-13', '1077', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-04-13', '1077', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-04-13', '753', 'ATL', 'DTW'),\n",
       "  ('DL', '2015-04-14', '188', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-04-14', '2051', 'DTW', 'RSW'),\n",
       "  ('DL', '2015-04-14', '2051', 'RSW', 'DTW'),\n",
       "  ('DL', '2015-04-14', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-04-14', '653', 'MSP', 'LAX'),\n",
       "  ('DL', '2015-04-15', '1109', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-04-15', '1352', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-04-15', '2020', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-04-15', '2264', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-04-16', '2474', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-04-16', '2634', 'JFK', 'DEN'),\n",
       "  ('DL', '2015-04-16', '499', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-04-17', '1610', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-04-17', '1847', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-04-17', '281', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-04-18', '822', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-04-18', '822', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-04-19', '1511', 'DTW', 'DEN'),\n",
       "  ('DL', '2015-04-19', '1934', 'SLC', 'BIL'),\n",
       "  ('DL', '2015-04-19', '2182', 'DEN', 'JFK'),\n",
       "  ('DL', '2015-04-19', '470', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-04-20', '1471', 'DTW', 'AUS'),\n",
       "  ('DL', '2015-04-20', '1912', 'BIL', 'SLC'),\n",
       "  ('DL', '2015-04-20', '2312', 'JFK', 'DTW'),\n",
       "  ('DL', '2015-04-20', '466', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-04-21', '1984', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-04-22', '439', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-04-23', '1211', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-04-23', '1936', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-04-23', '2404', 'SAN', 'JFK'),\n",
       "  ('DL', '2015-04-23', '460', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-04-24', '2315', 'CVG', 'ATL'),\n",
       "  ('DL', '2015-04-24', '421', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-04-25', '1972', 'AUS', 'JFK'),\n",
       "  ('DL', '2015-04-26', '1830', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-04-26', '492', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-04-26', '622', 'PDX', 'SLC'),\n",
       "  ('DL', '2015-04-26', '976', 'SLC', 'PDX'),\n",
       "  ('DL', '2015-04-28', '1077', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-04-28', '1077', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-04-29', '1165', 'ATL', 'CHS'),\n",
       "  ('DL', '2015-04-29', '1165', 'CHS', 'ATL'),\n",
       "  ('DL', '2015-04-29', '1684', 'ATL', 'SJC'),\n",
       "  ('DL', '2015-04-29', '1684', 'SJC', 'ATL'),\n",
       "  ('DL', '2015-04-30', '421', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-05-01', '1302', 'ATL', 'SDF'),\n",
       "  ('DL', '2015-05-01', '1302', 'SDF', 'ATL'),\n",
       "  ('DL', '2015-05-01', '1713', 'ATL', 'RSW'),\n",
       "  ('DL', '2015-05-01', '1984', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-05-01', '2524', 'ATL', 'VPS'),\n",
       "  ('DL', '2015-05-01', '2524', 'VPS', 'ATL'),\n",
       "  ('DL', '2015-05-02', '2199', 'RSW', 'DTW'),\n",
       "  ('DL', '2015-05-03', '1576', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-05-03', '159', 'BOS', 'DTW'),\n",
       "  ('DL', '2015-05-03', '1621', 'DTW', 'TPA'),\n",
       "  ('DL', '2015-05-03', '2052', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-05-03', '2523', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-05-04', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-05-04', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-05-04', '1168', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-05-04', '1819', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-05-05', '1169', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-05-05', '2345', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-05-06', '2002', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-05-06', '2583', 'CMH', 'ATL'),\n",
       "  ('DL', '2015-05-06', '766', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-05-06', '891', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-05-07', '2059', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-05-07', '2155', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-05-08', '1167', 'SLC', 'BZN'),\n",
       "  ('DL', '2015-05-08', '2140', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-05-08', '2155', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-05-08', '443', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-05-09', '1467', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-05-09', '2526', 'BZN', 'SLC'),\n",
       "  ('DL', '2015-05-09', '437', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-05-09', '466', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-05-10', '198', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-05-10', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-05-10', '281', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-05-11', '1325', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-05-11', '2215', 'MSP', 'FSD'),\n",
       "  ('DL', '2015-05-11', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-05-11', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-05-12', '1957', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-05-12', '2004', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-05-12', '2092', 'MSP', 'MSY'),\n",
       "  ('DL', '2015-05-12', '896', 'FSD', 'MSP'),\n",
       "  ('DL', '2015-05-12', '984', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-05-13', '1168', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-05-13', '129', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-05-13', '1634', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-05-13', '1877', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-05-13', '2122', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-05-13', '2170', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-05-14', '1169', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-05-14', '2345', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-05-15', '2583', 'CMH', 'ATL'),\n",
       "  ('DL', '2015-05-16', '1557', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-05-16', '1755', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-05-16', '2004', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-05-17', '1358', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-05-18', '1063', 'ATL', 'BHM'),\n",
       "  ('DL', '2015-05-18', '1423', 'ATL', 'STL'),\n",
       "  ('DL', '2015-05-18', '1423', 'STL', 'ATL'),\n",
       "  ('DL', '2015-05-18', '1800', 'ATL', 'BOS'),\n",
       "  ('DL', '2015-05-18', '2482', 'BOS', 'ATL'),\n",
       "  ('DL', '2015-05-19', '2002', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-05-19', '667', 'BHM', 'ATL'),\n",
       "  ('DL', '2015-05-19', '766', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-05-20', '1621', 'DTW', 'TPA'),\n",
       "  ('DL', '2015-05-21', '442', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-05-22', '1835', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-05-22', '1984', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-05-23', '1417', 'ATL', 'CVG'),\n",
       "  ('DL', '2015-05-23', '2324', 'SLC', 'ATL'),\n",
       "  ('DL', '2015-05-24', '1581', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-05-24', '1933', 'CVG', 'LAX'),\n",
       "  ('DL', '2015-05-25', '1405', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-05-25', '1502', 'CVG', 'TPA'),\n",
       "  ('DL', '2015-05-25', '1502', 'TPA', 'CVG'),\n",
       "  ('DL', '2015-05-25', '1833', 'CVG', 'LAX'),\n",
       "  ('DL', '2015-05-26', '1806', 'LAX', 'DTW'),\n",
       "  ('DL', '2015-05-26', '2020', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-05-26', '2335', 'DTW', 'DEN'),\n",
       "  ('DL', '2015-05-27', '1610', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-05-27', '1728', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-05-27', '343', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-05-28', '2217', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-05-28', '2405', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-05-28', '2435', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-05-28', '2492', 'PHX', 'DTW'),\n",
       "  ('DL', '2015-05-29', '1167', 'SLC', 'BZN'),\n",
       "  ('DL', '2015-05-29', '1461', 'ORD', 'SLC'),\n",
       "  ('DL', '2015-05-29', '1461', 'SLC', 'ORD'),\n",
       "  ('DL', '2015-05-29', '2034', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-05-29', '2316', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-05-30', '1327', 'PHX', 'ATL'),\n",
       "  ('DL', '2015-05-30', '2526', 'BZN', 'SLC'),\n",
       "  ('DL', '2015-05-30', '2569', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-05-30', '466', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-05-31', '1237', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-05-31', '2002', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-05-31', '766', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-06-01', '1217', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-06-01', '866', 'BOS', 'DTW'),\n",
       "  ('DL', '2015-06-02', '2155', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-06-03', '443', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-06-04', '1602', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-06-06', '1693', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-06-06', '2849', 'BNA', 'LAX'),\n",
       "  ('DL', '2015-06-06', '2849', 'LAX', 'BNA'),\n",
       "  ('DL', '2015-06-07', '2137', 'TPA', 'ATL'),\n",
       "  ('DL', '2015-06-07', '843', 'ATL', 'JFK'),\n",
       "  ('DL', '2015-06-08', '458', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-06-09', '1972', 'AUS', 'JFK'),\n",
       "  ('DL', '2015-06-10', '2835', 'JFK', 'SAT'),\n",
       "  ('DL', '2015-06-10', '2836', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-06-10', '401', 'AUS', 'JFK'),\n",
       "  ('DL', '2015-06-11', '1532', 'SAT', 'JFK'),\n",
       "  ('DL', '2015-06-11', '1901', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-06-11', '495', 'JFK', 'MIA'),\n",
       "  ('DL', '2015-06-12', '1432', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-06-12', '2820', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-06-12', '530', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-06-12', '530', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-06-13', '2188', 'ATL', 'SEA'),\n",
       "  ('DL', '2015-06-13', '2188', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-06-14', '1781', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-06-14', '2179', 'SLC', 'SMF'),\n",
       "  ('DL', '2015-06-14', '707', 'ORD', 'SLC'),\n",
       "  ('DL', '2015-06-14', '707', 'SLC', 'ORD'),\n",
       "  ('DL', '2015-06-14', '786', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-06-15', '1536', 'BWI', 'DTW'),\n",
       "  ('DL', '2015-06-15', '1536', 'DTW', 'BWI'),\n",
       "  ('DL', '2015-06-15', '188', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-06-15', '1936', 'SMF', 'MSP'),\n",
       "  ('DL', '2015-06-15', '2264', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-06-15', '2532', 'BOS', 'LAX'),\n",
       "  ('DL', '2015-06-15', '557', 'MSP', 'DTW'),\n",
       "  ('DL', '2015-06-16', '1543', 'MSY', 'MSP'),\n",
       "  ('DL', '2015-06-16', '1577', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-06-16', '524', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-06-17', '2051', 'MSP', 'LAS'),\n",
       "  ('DL', '2015-06-17', '2812', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-06-17', '779', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-06-18', '1602', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-06-18', '2084', 'ATL', 'STL'),\n",
       "  ('DL', '2015-06-18', '2084', 'STL', 'ATL'),\n",
       "  ('DL', '2015-06-19', '2239', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-06-19', '835', 'SLC', 'ATL'),\n",
       "  ('DL', '2015-06-21', '198', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-06-21', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-06-21', '759', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-06-22', '2497', 'DEN', 'DTW'),\n",
       "  ('DL', '2015-06-22', '2842', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-06-22', '300', 'SLC', 'DEN'),\n",
       "  ('DL', '2015-06-22', '770', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-06-23', '1737', 'BWI', 'DTW'),\n",
       "  ('DL', '2015-06-23', '1737', 'DTW', 'BWI'),\n",
       "  ('DL', '2015-06-23', '1795', 'DTW', 'PHX'),\n",
       "  ('DL', '2015-06-24', '1600', 'SLC', 'BZN'),\n",
       "  ('DL', '2015-06-24', '1994', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-06-24', '2140', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-06-24', '443', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-06-25', '1217', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-06-25', '134', 'PHX', 'DTW'),\n",
       "  ('DL', '2015-06-25', '2067', 'SLC', 'PHX'),\n",
       "  ('DL', '2015-06-25', '2526', 'BZN', 'SLC'),\n",
       "  ('DL', '2015-06-25', '2796', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-06-26', '1901', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-06-26', '454', 'JFK', 'ATL'),\n",
       "  ('DL', '2015-06-26', '495', 'JFK', 'MIA'),\n",
       "  ('DL', '2015-06-26', '843', 'ATL', 'JFK'),\n",
       "  ('DL', '2015-06-27', '2849', 'BNA', 'LAX'),\n",
       "  ('DL', '2015-06-27', '2849', 'LAX', 'BNA'),\n",
       "  ('DL', '2015-06-28', '1432', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-06-28', '1653', 'SEA', 'FAI'),\n",
       "  ('DL', '2015-06-28', '943', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-06-28', '943', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-06-29', '1578', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-06-29', '1611', 'FAI', 'SEA'),\n",
       "  ('DL', '2015-06-29', '523', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-06-30', '1558', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-06-30', '1845', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-06-30', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-07-01', '1357', 'MSP', 'DTW'),\n",
       "  ('DL', '2015-07-02', '1231', 'DCA', 'DTW'),\n",
       "  ('DL', '2015-07-02', '1232', 'DTW', 'STL'),\n",
       "  ('DL', '2015-07-02', '1232', 'STL', 'DTW'),\n",
       "  ('DL', '2015-07-02', '1737', 'BWI', 'DTW'),\n",
       "  ('DL', '2015-07-02', '1737', 'DTW', 'BWI'),\n",
       "  ('DL', '2015-07-02', '2144', 'DTW', 'DCA'),\n",
       "  ('DL', '2015-07-03', '1749', 'DTW', 'PHL'),\n",
       "  ('DL', '2015-07-04', '1067', 'DTW', 'PDX'),\n",
       "  ('DL', '2015-07-04', '1238', 'DTW', 'MSY'),\n",
       "  ('DL', '2015-07-04', '1238', 'MSY', 'DTW'),\n",
       "  ('DL', '2015-07-04', '1322', 'PHL', 'DTW'),\n",
       "  ('DL', '2015-07-05', '1156', 'PDX', 'ATL'),\n",
       "  ('DL', '2015-07-05', '1791', 'ATL', 'CHS'),\n",
       "  ('DL', '2015-07-05', '1791', 'CHS', 'ATL'),\n",
       "  ('DL', '2015-07-05', '503', 'ATL', 'PDX'),\n",
       "  ('DL', '2015-07-05', '503', 'PDX', 'ATL'),\n",
       "  ('DL', '2015-07-06', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-07-06', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-07-06', '1255', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-07-08', '1905', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-07-08', '198', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-07-08', '2592', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-07-09', '1805', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-07-09', '301', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-07-09', '545', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-07-09', '984', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-07-10', '1807', 'CVG', 'SEA'),\n",
       "  ('DL', '2015-07-10', '2258', 'SEA', 'FAI'),\n",
       "  ('DL', '2015-07-10', '943', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-07-10', '943', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-07-11', '1304', 'SLC', 'ATL'),\n",
       "  ('DL', '2015-07-11', '1611', 'FAI', 'SEA'),\n",
       "  ('DL', '2015-07-11', '1682', 'CVG', 'SLC'),\n",
       "  ('DL', '2015-07-11', '1974', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-07-11', '2640', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-07-12', '2188', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-07-12', '2841', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-07-13', '1470', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-07-13', '1781', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-07-13', '530', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-07-13', '530', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-07-14', '1794', 'PDX', 'MSP'),\n",
       "  ('DL', '2015-07-14', '773', 'ATL', 'PDX'),\n",
       "  ('DL', '2015-07-15', '1975', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-07-15', '2406', 'MSP', 'MSY'),\n",
       "  ('DL', '2015-07-15', '2530', 'LAX', 'DTW'),\n",
       "  ('DL', '2015-07-16', '1737', 'BWI', 'DTW'),\n",
       "  ('DL', '2015-07-16', '1737', 'DTW', 'BWI'),\n",
       "  ('DL', '2015-07-16', '1821', 'DTW', 'PHX'),\n",
       "  ('DL', '2015-07-16', '2201', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-07-17', '488', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-07-18', '1994', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-07-19', '451', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-07-20', '2609', 'JFK', 'MIA'),\n",
       "  ('DL', '2015-07-20', '326', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-07-20', '42', 'MIA', 'JFK'),\n",
       "  ('DL', '2015-07-22', '1841', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-07-22', '2474', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-07-25', '772', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-07-26', '1060', 'SLC', 'DFW'),\n",
       "  ('DL', '2015-07-26', '1795', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-07-26', '2190', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-07-27', '1217', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-07-27', '134', 'PHX', 'DTW'),\n",
       "  ('DL', '2015-07-27', '1728', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-07-27', '2067', 'SLC', 'PHX'),\n",
       "  ('DL', '2015-07-27', '857', 'DFW', 'SLC'),\n",
       "  ('DL', '2015-07-28', '2435', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-07-28', '2791', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-07-28', '401', 'AUS', 'JFK'),\n",
       "  ('DL', '2015-07-29', '1217', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-07-29', '2576', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-07-30', '1602', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-07-30', '2046', 'ATL', 'PHX'),\n",
       "  ('DL', '2015-07-31', '199', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-07-31', '2202', 'PHX', 'LAX'),\n",
       "  ('DL', '2015-07-31', '2220', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-07-31', '848', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-08-01', '459', 'JFK', 'BOS'),\n",
       "  ('DL', '2015-08-02', '1627', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-08-02', '2391', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-08-02', '710', 'MSP', 'PDX'),\n",
       "  ('DL', '2015-08-03', '2842', 'ATL', 'COS'),\n",
       "  ('DL', '2015-08-04', '1789', 'ATL', 'JAX'),\n",
       "  ('DL', '2015-08-04', '1789', 'JAX', 'ATL'),\n",
       "  ('DL', '2015-08-04', '2114', 'COS', 'ATL'),\n",
       "  ('DL', '2015-08-04', '347', 'ATL', 'ORD'),\n",
       "  ('DL', '2015-08-04', '347', 'ORD', 'ATL'),\n",
       "  ('DL', '2015-08-04', '772', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-08-05', '1280', 'SLC', 'MSP'),\n",
       "  ('DL', '2015-08-05', '1391', 'LAS', 'ATL'),\n",
       "  ('DL', '2015-08-05', '1450', 'LAS', 'MSP'),\n",
       "  ('DL', '2015-08-05', '2830', 'MSP', 'LAS'),\n",
       "  ('DL', '2015-08-05', '787', 'MSP', 'SLC'),\n",
       "  ('DL', '2015-08-06', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-08-06', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-08-06', '1255', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-08-06', '545', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-08-07', '1807', 'CVG', 'SEA'),\n",
       "  ('DL', '2015-08-07', '2258', 'SEA', 'FAI'),\n",
       "  ('DL', '2015-08-07', '943', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-08-07', '943', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-08-08', '1611', 'FAI', 'SEA'),\n",
       "  ('DL', '2015-08-08', '1682', 'CVG', 'SLC'),\n",
       "  ('DL', '2015-08-08', '1974', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-08-08', '2640', 'LAX', 'CVG'),\n",
       "  ('DL', '2015-08-09', '2579', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-08-09', '2606', 'SAN', 'JFK'),\n",
       "  ('DL', '2015-08-09', '466', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-08-10', '2836', 'JFK', 'PDX'),\n",
       "  ('DL', '2015-08-11', '1909', 'SLC', 'SAN'),\n",
       "  ('DL', '2015-08-11', '2140', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-08-11', '400', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-08-11', '443', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-08-12', '1189', 'BWI', 'SLC'),\n",
       "  ('DL', '2015-08-12', '1189', 'SLC', 'BWI'),\n",
       "  ('DL', '2015-08-12', '1337', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-08-12', '2020', 'LAX', 'MEM'),\n",
       "  ('DL', '2015-08-12', '978', 'SAN', 'SLC'),\n",
       "  ('DL', '2015-08-13', '1577', 'LAX', 'MSY'),\n",
       "  ('DL', '2015-08-13', '1627', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-08-13', '710', 'MSP', 'PDX'),\n",
       "  ('DL', '2015-08-13', '713', 'MSY', 'MSP'),\n",
       "  ('DL', '2015-08-13', '838', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-08-14', '2842', 'ATL', 'COS'),\n",
       "  ('DL', '2015-08-15', '1949', 'ATL', 'MIA'),\n",
       "  ('DL', '2015-08-15', '2114', 'COS', 'ATL'),\n",
       "  ('DL', '2015-08-16', '1728', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-08-16', '1883', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-08-16', '459', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-08-16', '782', 'MIA', 'JFK'),\n",
       "  ('DL', '2015-08-17', '445', 'JFK', 'AUS'),\n",
       "  ('DL', '2015-08-18', '1972', 'AUS', 'JFK'),\n",
       "  ('DL', '2015-08-19', '953', 'ATL', 'SAT'),\n",
       "  ('DL', '2015-08-20', '873', 'SAT', 'JFK'),\n",
       "  ('DL', '2015-08-21', '1841', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-08-21', '2571', 'JFK', 'SAT'),\n",
       "  ('DL', '2015-08-21', '447', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-08-22', '873', 'SAT', 'JFK'),\n",
       "  ('DL', '2015-08-23', '1841', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-08-23', '2784', 'JFK', 'SAT'),\n",
       "  ('DL', '2015-08-23', '447', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-08-24', '1448', 'ATL', 'DTW'),\n",
       "  ('DL', '2015-08-24', '774', 'SAT', 'ATL'),\n",
       "  ('DL', '2015-08-25', '2066', 'MSP', 'LAS'),\n",
       "  ('DL', '2015-08-25', '2391', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-08-25', '2437', 'DTW', 'BOS'),\n",
       "  ('DL', '2015-08-26', '1795', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-08-26', '485', 'JFK', 'ATL'),\n",
       "  ('DL', '2015-08-28', '1357', 'MSP', 'DTW'),\n",
       "  ('DL', '2015-08-28', '2223', 'MSP', 'ORD'),\n",
       "  ('DL', '2015-08-28', '2223', 'ORD', 'MSP'),\n",
       "  ('DL', '2015-08-29', '1056', 'SEA', 'CVG'),\n",
       "  ('DL', '2015-08-29', '2045', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-08-29', '2045', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-08-29', '281', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-08-30', '1807', 'CVG', 'SEA'),\n",
       "  ('DL', '2015-08-30', '2188', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-08-30', '943', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-08-30', '943', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-08-31', '1781', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-08-31', '384', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-08-31', '530', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-08-31', '530', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-09-01', '1553', 'ATL', 'DFW'),\n",
       "  ('DL', '2015-09-01', '30', 'DFW', 'ATL'),\n",
       "  ('DL', '2015-09-28', '1534', 'IND', 'ATL'),\n",
       "  ('DL', '2015-09-29', '1402', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-09-29', '1529', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-09-30', '1151', 'MSP', 'SFO'),\n",
       "  ('DL', '2015-09-30', '1998', 'SFO', 'SLC'),\n",
       "  ('DL', '2015-09-30', '2175', 'JFK', 'MSP'),\n",
       "  ('DL', '2015-09-30', '2619', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-09-30', '779', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-10-01', '2833', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-10-01', '403', 'SAN', 'JFK'),\n",
       "  ('DL', '2015-10-02', '1210', 'DTW', 'RDU'),\n",
       "  ('DL', '2015-10-02', '2122', 'PHX', 'DTW'),\n",
       "  ('DL', '2015-10-02', '425', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-10-03', '1124', 'LAX', 'PHX'),\n",
       "  ('DL', '2015-10-03', '2618', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-10-04', '1128', 'PHX', 'ATL'),\n",
       "  ('DL', '2015-10-04', '2330', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-10-04', '662', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-10-04', '739', 'LAX', 'PHX'),\n",
       "  ('DL', '2015-10-05', '1429', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-10-05', '1994', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-10-05', '439', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-10-06', '403', 'SAN', 'JFK'),\n",
       "  ('DL', '2015-10-06', '437', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-10-07', '29', 'ATL', 'DFW'),\n",
       "  ('DL', '2015-10-08', '1890', 'DFW', 'ATL'),\n",
       "  ('DL', '2015-10-08', '2556', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-10-09', '2196', 'SLC', 'MCO'),\n",
       "  ('DL', '2015-10-09', '2332', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-10-09', '2618', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-10-10', '1332', 'CVG', 'LAS'),\n",
       "  ('DL', '2015-10-10', '842', 'SLC', 'CVG'),\n",
       "  ('DL', '2015-10-10', '995', 'MCO', 'SLC'),\n",
       "  ('DL', '2015-10-11', '1490', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-10-11', '1932', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-10-11', '780', 'LAS', 'MSP'),\n",
       "  ('DL', '2015-10-12', '1280', 'SLC', 'MSP'),\n",
       "  ('DL', '2015-10-12', '1728', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-10-12', '2051', 'MSP', 'LAS'),\n",
       "  ('DL', '2015-10-12', '787', 'MSP', 'SLC'),\n",
       "  ('DL', '2015-10-13', '2153', 'JFK', 'PHX'),\n",
       "  ('DL', '2015-10-13', '2217', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-10-13', '511', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-10-14', '1529', 'LAS', 'CVG'),\n",
       "  ('DL', '2015-10-14', '1994', 'PHX', 'JFK'),\n",
       "  ('DL', '2015-10-14', '439', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-10-15', '2315', 'CVG', 'ATL'),\n",
       "  ('DL', '2015-10-19', '1740', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-10-19', '1830', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-10-19', '421', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-10-20', '1740', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-10-20', '421', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-10-20', '662', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-10-20', '739', 'LAX', 'PHX'),\n",
       "  ('DL', '2015-10-21', '1128', 'PHX', 'ATL'),\n",
       "  ('DL', '2015-10-21', '1705', 'ATL', 'SFO'),\n",
       "  ('DL', '2015-10-21', '1705', 'SFO', 'ATL'),\n",
       "  ('DL', '2015-10-22', '1351', 'ATL', 'AUS'),\n",
       "  ('DL', '2015-10-22', '1351', 'AUS', 'ATL'),\n",
       "  ('DL', '2015-10-22', '2241', 'ATL', 'ORD'),\n",
       "  ('DL', '2015-10-23', '977', 'ORD', 'ATL'),\n",
       "  ('DL', '2015-10-27', '1374', 'OAK', 'SLC'),\n",
       "  ('DL', '2015-10-27', '1374', 'SLC', 'OAK'),\n",
       "  ('DL', '2015-10-27', '2436', 'SLC', 'DEN'),\n",
       "  ('DL', '2015-10-27', '453', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-10-28', '1072', 'SFO', 'SLC'),\n",
       "  ('DL', '2015-10-28', '1151', 'MSP', 'SFO'),\n",
       "  ('DL', '2015-10-28', '1429', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-10-28', '1716', 'DEN', 'MSP'),\n",
       "  ('DL', '2015-10-28', '779', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-10-29', '1841', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-10-29', '442', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-10-30', '1374', 'OAK', 'SLC'),\n",
       "  ('DL', '2015-10-30', '1374', 'SLC', 'OAK'),\n",
       "  ('DL', '2015-10-30', '453', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-10-30', '652', 'SLC', 'BZN'),\n",
       "  ('DL', '2015-10-31', '1158', 'SLC', 'MCO'),\n",
       "  ('DL', '2015-10-31', '2526', 'BZN', 'SLC'),\n",
       "  ('DL', '2015-11-01', '185', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-11-01', '2171', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-11-02', '1843', 'SLC', 'BOS'),\n",
       "  ('DL', '2015-11-02', '2005', 'ATL', 'DTW'),\n",
       "  ('DL', '2015-11-02', '2317', 'DTW', 'LAS'),\n",
       "  ('DL', '2015-11-02', '89', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-11-03', '1542', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-11-03', '2083', 'BOS', 'SLC'),\n",
       "  ('DL', '2015-11-03', '444', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-11-03', '460', 'JFK', 'SEA'),\n",
       "  ('DL', '2015-11-04', '549', 'ATL', 'SJU'),\n",
       "  ('DL', '2015-11-05', '147', 'SLC', 'ATL'),\n",
       "  ('DL', '2015-11-05', '1507', 'ATL', 'SLC'),\n",
       "  ('DL', '2015-11-05', '2831', 'ATL', 'MCI'),\n",
       "  ('DL', '2015-11-05', '922', 'SJU', 'ATL'),\n",
       "  ('DL', '2015-11-06', '1630', 'ATL', 'SAT'),\n",
       "  ('DL', '2015-11-06', '2165', 'MCI', 'ATL'),\n",
       "  ('DL', '2015-11-07', '303', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-11-07', '774', 'SAT', 'ATL'),\n",
       "  ('DL', '2015-11-08', '1482', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-11-09', '882', 'ATL', 'MCI'),\n",
       "  ('DL', '2015-11-10', '2165', 'MCI', 'ATL'),\n",
       "  ('DL', '2015-11-11', '1786', 'ATL', 'LGA'),\n",
       "  ('DL', '2015-11-11', '847', 'LGA', 'ATL'),\n",
       "  ('DL', '2015-11-12', '1211', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-11-12', '2004', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-11-13', '1457', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-11-13', '1585', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-11-13', '2157', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-11-13', '2377', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-11-15', '303', 'ATL', 'RDU'),\n",
       "  ('DL', '2015-11-16', '1137', 'RDU', 'ATL'),\n",
       "  ('DL', '2015-11-16', '1402', 'ATL', 'LAS'),\n",
       "  ('DL', '2015-11-16', '2508', 'LAS', 'JFK'),\n",
       "  ('DL', '2015-11-17', '2474', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-11-17', '2838', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-11-17', '42', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-11-18', '326', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-11-18', '332', 'SJU', 'JFK'),\n",
       "  ('DL', '2015-11-18', '446', 'JFK', 'SJU'),\n",
       "  ('DL', '2015-11-20', '1389', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-11-20', '459', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-11-21', '36', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-11-21', '935', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-11-22', '2303', 'JFK', 'PDX'),\n",
       "  ('DL', '2015-11-22', '2397', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-11-22', '444', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-11-22', '822', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-11-23', '1736', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-11-23', '483', 'JFK', 'SEA'),\n",
       "  ('DL', '2015-11-24', '1972', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-11-24', '405', 'JFK', 'LAS'),\n",
       "  ('DL', '2015-11-24', '761', 'LAS', 'CVG'),\n",
       "  ('DL', '2015-11-25', '1741', 'CVG', 'SLC'),\n",
       "  ('DL', '2015-11-25', '2303', 'JFK', 'PDX'),\n",
       "  ('DL', '2015-11-25', '444', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-11-26', '1831', 'PDX', 'SLC'),\n",
       "  ('DL', '2015-11-26', '1859', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-11-26', '2170', 'SLC', 'LAX'),\n",
       "  ('DL', '2015-11-26', '2825', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-11-27', '2200', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-11-28', '1169', 'MIA', 'LAX'),\n",
       "  ('DL', '2015-11-28', '1566', 'LAX', 'RDU'),\n",
       "  ('DL', '2015-11-28', '2787', 'BNA', 'LAX'),\n",
       "  ('DL', '2015-11-28', '2787', 'LAX', 'BNA'),\n",
       "  ('DL', '2015-11-29', '1170', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-11-29', '2618', 'RDU', 'LAX'),\n",
       "  ('DL', '2015-11-30', '1327', 'CMH', 'LAX'),\n",
       "  ('DL', '2015-11-30', '2034', 'MIA', 'JFK'),\n",
       "  ('DL', '2015-11-30', '2200', 'LAX', 'MIA'),\n",
       "  ('DL', '2015-12-01', '1473', 'SEA', 'JFK'),\n",
       "  ('DL', '2015-12-01', '2303', 'JFK', 'PDX'),\n",
       "  ('DL', '2015-12-01', '428', 'JFK', 'SEA'),\n",
       "  ('DL', '2015-12-02', '1504', 'PDX', 'JFK'),\n",
       "  ('DL', '2015-12-02', '483', 'JFK', 'SEA'),\n",
       "  ('DL', '2015-12-03', '2397', 'SEA', 'SLC'),\n",
       "  ('DL', '2015-12-03', '444', 'SLC', 'JFK'),\n",
       "  ('DL', '2015-12-03', '446', 'JFK', 'SAN'),\n",
       "  ('DL', '2015-12-04', '1260', 'SLC', 'PDX'),\n",
       "  ('DL', '2015-12-04', '2404', 'SAN', 'JFK'),\n",
       "  ('DL', '2015-12-04', '431', 'JFK', 'SLC'),\n",
       "  ('DL', '2015-12-05', '1074', 'SLC', 'MSP'),\n",
       "  ('DL', '2015-12-05', '1683', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-12-05', '1980', 'PDX', 'SLC'),\n",
       "  ('DL', '2015-12-05', '89', 'LAS', 'SLC'),\n",
       "  ('DL', '2015-12-06', '1454', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-12-06', '1577', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-12-06', '2839', 'MSP', 'MSY'),\n",
       "  ('DL', '2015-12-07', '1204', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-12-07', '2428', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-12-08', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-12-08', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-12-08', '1558', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-12-09', '1654', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-12-10', '1204', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-12-10', '2428', 'LAX', 'TPA'),\n",
       "  ('DL', '2015-12-11', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-12-11', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-12-11', '1558', 'TPA', 'LAX'),\n",
       "  ('DL', '2015-12-12', '1554', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-12-13', '1061', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-12-13', '1061', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-12-13', '1204', 'ATL', 'LAX'),\n",
       "  ('DL', '2015-12-13', '186', 'LAX', 'MCO'),\n",
       "  ('DL', '2015-12-14', '185', 'MCO', 'LAX'),\n",
       "  ('DL', '2015-12-14', '2787', 'BNA', 'LAX'),\n",
       "  ('DL', '2015-12-14', '2787', 'LAX', 'BNA'),\n",
       "  ('DL', '2015-12-15', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-12-16', '2145', 'SLC', 'SEA'),\n",
       "  ('DL', '2015-12-16', '2531', 'BOS', 'LAX'),\n",
       "  ('DL', '2015-12-16', '36', 'SEA', 'ANC'),\n",
       "  ('DL', '2015-12-16', '671', 'LAX', 'SLC'),\n",
       "  ('DL', '2015-12-17', '1384', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-12-17', '142', 'ANC', 'SEA'),\n",
       "  ('DL', '2015-12-17', '1549', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-12-17', '2019', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-12-18', '1487', 'DTW', 'MEM'),\n",
       "  ('DL', '2015-12-18', '2073', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-12-18', '2230', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-12-19', '1678', 'MSP', 'LAX'),\n",
       "  ('DL', '2015-12-19', '2020', 'MEM', 'LAX'),\n",
       "  ('DL', '2015-12-19', '2534', 'LAX', 'BOS'),\n",
       "  ('DL', '2015-12-19', '2549', 'BOS', 'MSP'),\n",
       "  ('DL', '2015-12-20', '1549', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-12-20', '1727', 'LAX', 'SEA'),\n",
       "  ('DL', '2015-12-21', '2230', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-12-21', '2413', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-12-22', '1321', 'MSP', 'BOI'),\n",
       "  ('DL', '2015-12-22', '1668', 'LAX', 'MSP'),\n",
       "  ('DL', '2015-12-23', '1316', 'BOI', 'MSP'),\n",
       "  ('DL', '2015-12-23', '1454', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-12-23', '1577', 'MSY', 'LAX'),\n",
       "  ('DL', '2015-12-23', '2839', 'MSP', 'MSY'),\n",
       "  ('DL', '2015-12-24', '2414', 'ATL', 'SEA'),\n",
       "  ('DL', '2015-12-25', '1549', 'SEA', 'JNU'),\n",
       "  ('DL', '2015-12-25', '2791', 'SEA', 'DTW'),\n",
       "  ('DL', '2015-12-25', '662', 'DTW', 'SEA'),\n",
       "  ('DL', '2015-12-26', '1554', 'LAX', 'ATL'),\n",
       "  ('DL', '2015-12-26', '2230', 'JNU', 'SEA'),\n",
       "  ('DL', '2015-12-26', '2413', 'SEA', 'LAX'),\n",
       "  ('DL', '2015-12-27', '2117', 'ATL', 'COS'),\n",
       "  ('DL', '2015-12-28', '1613', 'ATL', 'IND'),\n",
       "  ('DL', '2015-12-28', '1613', 'IND', 'ATL'),\n",
       "  ('DL', '2015-12-28', '2114', 'COS', 'ATL'),\n",
       "  ('DL', '2015-12-29', '1241', 'SLC', 'BZN'),\n",
       "  ('DL', '2015-12-29', '1782', 'MSP', 'SLC'),\n",
       "  ('DL', '2015-12-29', '721', 'ATL', 'MSP'),\n",
       "  ('DL', '2015-12-30', '1170', 'LAX', 'CMH'),\n",
       "  ('DL', '2015-12-30', '1208', 'BZN', 'SLC'),\n",
       "  ('DL', '2015-12-30', '1223', 'DTW', 'LAX'),\n",
       "  ('DL', '2015-12-30', '1483', 'SLC', 'LAS'),\n",
       "  ('DL', '2015-12-30', '1545', 'LAS', 'DTW'),\n",
       "  ('DL', '2015-12-31', '1327', 'CMH', 'LAX'),\n",
       "  ('DL', '2015-12-31', '1431', 'MSY', 'MSP'),\n",
       "  ('DL', '2015-12-31', '2340', 'LAX', 'MSY')]}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter down to the fields we need to identify and link to a flight\n",
    "flights = on_time_dataframe.rdd.map(\n",
    "    lambda x: (x.Carrier, x.FlightDate, x.FlightNum, x.Origin, x.Dest, x.TailNum)\n",
    ")\n",
    "\n",
    "# Group flights by tail number, sorted by date, then flight number, then origin/dest\n",
    "flights_per_airplane = flights\\\n",
    "  .map(lambda nameTuple: (nameTuple[5], [nameTuple[0:5]]))\\\n",
    "  .reduceByKey(lambda a, b: a + b)\\\n",
    "  .map(lambda tuple:\n",
    "      {\n",
    "        'TailNum': tuple[0], \n",
    "        'Flights': sorted(tuple[1], key=lambda x: (x[1], x[2], x[3], x[4]))\n",
    "      }\n",
    "    )\n",
    "flights_per_airplane.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counting Late Flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2,125,618\n",
      "2,086,896\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "35.9"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_flights = on_time_dataframe.count()\n",
    "\n",
    "# Flights that were late leaving...\n",
    "late_departures = on_time_dataframe.filter(\n",
    "  on_time_dataframe.DepDelayMinutes > 0\n",
    ")\n",
    "total_late_departures = late_departures.count()\n",
    "print(f'{total_late_departures:,}')\n",
    "\n",
    "# Flights that were late arriving...\n",
    "late_arrivals = on_time_dataframe.filter(\n",
    "  on_time_dataframe.ArrDelayMinutes > 0\n",
    ")\n",
    "total_late_arrivals = late_arrivals.count()\n",
    "print(f'{total_late_arrivals:,}')\n",
    "\n",
    "# Get the percentage of flights that are late, rounded to 1 decimal place\n",
    "pct_late = round((total_late_arrivals / (total_flights * 1.0)) * 100, 1)\n",
    "pct_late"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Counting Flights with Hero Captains\n",
    "\n",
    "\"Hero Captains\" are those that depart late but make up time in the air and arrive on time or early."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "606,902\n"
     ]
    }
   ],
   "source": [
    "# Flights that left late but made up time to arrive on time...\n",
    "on_time_heros = on_time_dataframe.filter(\n",
    "  (on_time_dataframe.DepDelayMinutes > 0)\n",
    "  &\n",
    "  (on_time_dataframe.ArrDelayMinutes <= 0)\n",
    ")\n",
    "total_on_time_heros = on_time_heros.count()\n",
    "print(f'{total_on_time_heros:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Printing Our Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total flights:   5,819,079\n",
      "Late departures: 2,125,618\n",
      "Late arrivals:   2,086,896\n",
      "Recoveries:      606,902\n",
      "Percentage Late: 35.9%\n"
     ]
    }
   ],
   "source": [
    "print(\"Total flights:   {:,}\".format(total_flights))\n",
    "print(\"Late departures: {:,}\".format(total_late_departures))\n",
    "print(\"Late arrivals:   {:,}\".format(total_late_arrivals))\n",
    "print(\"Recoveries:      {:,}\".format(total_on_time_heros))\n",
    "print(\"Percentage Late: {}%\".format(pct_late))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing the Average Lateness Per Flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------+-----------+\n",
      "|AvgDepDelay|AvgArrDelay|\n",
      "+-----------+-----------+\n",
      "|        9.4|        4.4|\n",
      "+-----------+-----------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Get the average minutes late departing and arriving\n",
    "spark.sql(\"\"\"\n",
    "SELECT\n",
    "  ROUND(AVG(DepDelay),1) AS AvgDepDelay,\n",
    "  ROUND(AVG(ArrDelay),1) AS AvgArrDelay\n",
    "FROM on_time_performance\n",
    "\"\"\"\n",
    ").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inspecting Late Flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------------+------------+------------+--------+-------------+-----------------+\n",
      "|ArrDelayMinutes|WeatherDelay|CarrierDelay|NASDelay|SecurityDelay|LateAircraftDelay|\n",
      "+---------------+------------+------------+--------+-------------+-----------------+\n",
      "|           31.0|         0.0|        31.0|     0.0|          0.0|              0.0|\n",
      "|           61.0|         0.0|         0.0|     0.0|          0.0|             61.0|\n",
      "|           17.0|         0.0|         0.0|    17.0|          0.0|              0.0|\n",
      "|          310.0|         0.0|        75.0|     0.0|          0.0|            235.0|\n",
      "|           17.0|         0.0|         0.0|     2.0|          0.0|             15.0|\n",
      "|           56.0|         0.0|        30.0|     0.0|          0.0|             26.0|\n",
      "|           17.0|         0.0|        14.0|     3.0|          0.0|              0.0|\n",
      "|          126.0|         0.0|         0.0|    21.0|          0.0|            105.0|\n",
      "|           29.0|         0.0|         0.0|     2.0|          0.0|             27.0|\n",
      "|           60.0|         0.0|        20.0|    36.0|          0.0|              4.0|\n",
      "|           20.0|         0.0|         0.0|    20.0|          0.0|              0.0|\n",
      "|           23.0|         0.0|        23.0|     0.0|          0.0|              0.0|\n",
      "|           88.0|         0.0|         0.0|     0.0|          0.0|             88.0|\n",
      "|           76.0|         0.0|         2.0|     0.0|          0.0|             74.0|\n",
      "|           25.0|         0.0|         6.0|    19.0|          0.0|              0.0|\n",
      "|           32.0|         0.0|        21.0|    11.0|          0.0|              0.0|\n",
      "|           69.0|         0.0|         1.0|    68.0|          0.0|              0.0|\n",
      "|           29.0|         0.0|         4.0|    25.0|          0.0|              0.0|\n",
      "|           22.0|         0.0|         8.0|    14.0|          0.0|              0.0|\n",
      "|           25.0|         0.0|         0.0|     0.0|          0.0|             25.0|\n",
      "+---------------+------------+------------+--------+-------------+-----------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Why are flights late? Lets look at some delayed flights and the delay causes\n",
    "late_flights = spark.sql(\"\"\"\n",
    "SELECT\n",
    "  ArrDelayMinutes,\n",
    "  WeatherDelay,\n",
    "  CarrierDelay,\n",
    "  NASDelay,\n",
    "  SecurityDelay,\n",
    "  LateAircraftDelay\n",
    "FROM\n",
    "  on_time_performance\n",
    "WHERE\n",
    "  WeatherDelay IS NOT NULL\n",
    "  OR\n",
    "  CarrierDelay IS NOT NULL\n",
    "  OR\n",
    "  NASDelay IS NOT NULL\n",
    "  OR\n",
    "  SecurityDelay IS NOT NULL\n",
    "  OR\n",
    "  LateAircraftDelay IS NOT NULL\n",
    "ORDER BY\n",
    "  FlightDate\n",
    "\"\"\")\n",
    "late_flights.sample(False, 0.01).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Determining Why Flights Are Late"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-----------------+-----------------+-------------+------------------+-----------------------+\n",
      "|pct_weather_delay|pct_carrier_delay|pct_nas_delay|pct_security_delay|pct_late_aircraft_delay|\n",
      "+-----------------+-----------------+-------------+------------------+-----------------------+\n",
      "|              4.5|             29.2|         20.7|               0.1|                   36.1|\n",
      "+-----------------+-----------------+-------------+------------------+-----------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate the percentage contribution to delay for each source\n",
    "total_delays = spark.sql(\"\"\"\n",
    "SELECT\n",
    "  ROUND(SUM(WeatherDelay)/SUM(ArrDelayMinutes) * 100, 1) AS pct_weather_delay,\n",
    "  ROUND(SUM(CarrierDelay)/SUM(ArrDelayMinutes) * 100, 1) AS pct_carrier_delay,\n",
    "  ROUND(SUM(NASDelay)/SUM(ArrDelayMinutes) * 100, 1) AS pct_nas_delay,\n",
    "  ROUND(SUM(SecurityDelay)/SUM(ArrDelayMinutes) * 100, 1) AS pct_security_delay,\n",
    "  ROUND(SUM(LateAircraftDelay)/SUM(ArrDelayMinutes) * 100, 1) AS pct_late_aircraft_delay\n",
    "FROM on_time_performance\n",
    "\"\"\")\n",
    "total_delays.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing a Histogram of Weather Delayed Flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([1, 5, 10, 15, 30, 60, 120, 240, 480, 720, 1440.0], [5436, 7668, 6636, 16007, 13569, 9442, 4598, 1136, 152, 72])\n"
     ]
    }
   ],
   "source": [
    "# Eyeball the first to define our buckets\n",
    "weather_delay_histogram = on_time_dataframe\\\n",
    "  .select(\"WeatherDelay\")\\\n",
    "  .rdd\\\n",
    "  .flatMap(lambda x: x)\\\n",
    "  .histogram([1, 5, 10, 15, 30, 60, 120, 240, 480, 720, 24*60.0])\n",
    "print(weather_delay_histogram)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# See above for definition\n",
    "create_hist(weather_delay_histogram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparing a Histogram for Visualization by d3.js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([0, 15, 30, 60, 120, 240, 480, 720, 1440.0], [19740, 16007, 13569, 9442, 4598, 1136, 152, 72])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'key': 1,\n",
       " 'data': [{'label': 0, 'value': 19740},\n",
       "  {'label': 15, 'value': 16007},\n",
       "  {'label': 30, 'value': 13569},\n",
       "  {'label': 60, 'value': 9442},\n",
       "  {'label': 120, 'value': 4598},\n",
       "  {'label': 240, 'value': 1136},\n",
       "  {'label': 480, 'value': 152},\n",
       "  {'label': 720, 'value': 72}]}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform the data into something easily consumed by d3\n",
    "def histogram_to_publishable(histogram):\n",
    "  record = {'key': 1, 'data': []}\n",
    "  for label, value in zip(histogram[0], histogram[1]):\n",
    "    record['data'].append(\n",
    "      {\n",
    "        'label': label,\n",
    "        'value': value\n",
    "      }\n",
    "    )\n",
    "  return record\n",
    "\n",
    "# Recompute the weather histogram with a filter for on-time flights\n",
    "weather_delay_histogram = on_time_dataframe\\\n",
    "  .filter(\n",
    "    (on_time_dataframe.WeatherDelay.isNotNull())\n",
    "    &\n",
    "    (on_time_dataframe.WeatherDelay > 0)\n",
    "  )\\\n",
    "  .select(\"WeatherDelay\")\\\n",
    "  .rdd\\\n",
    "  .flatMap(lambda x: x)\\\n",
    "  .histogram([0, 15, 30, 60, 120, 240, 480, 720, 24*60.0])\n",
    "print(weather_delay_histogram)\n",
    "\n",
    "record = histogram_to_publishable(weather_delay_histogram)\n",
    "record"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building a Classifier Model to Predict Flight Delays\n",
    "\n",
    "## Loading Our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row(ArrDelay=13.0, CRSArrTime=datetime.datetime(2015, 1, 1, 18, 10), CRSDepTime=datetime.datetime(2015, 1, 1, 15, 30), Carrier='AA', DayOfMonth=1, DayOfWeek=4, DayOfYear=1, DepDelay=14.0, Dest='DFW', Distance=569.0, FlightDate=datetime.date(2014, 12, 31), FlightNum='1024', Origin='ABQ')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pyspark.sql.types import StringType, IntegerType, FloatType, DoubleType, DateType, TimestampType\n",
    "from pyspark.sql.types import StructType, StructField\n",
    "from pyspark.sql.functions import udf\n",
    "\n",
    "schema = StructType([\n",
    "  StructField(\"ArrDelay\", DoubleType(), True),     # \"ArrDelay\":5.0\n",
    "  StructField(\"CRSArrTime\", TimestampType(), True),    # \"CRSArrTime\":\"2015-12-31T03:20:00.000-08:00\"\n",
    "  StructField(\"CRSDepTime\", TimestampType(), True),    # \"CRSDepTime\":\"2015-12-31T03:05:00.000-08:00\"\n",
    "  StructField(\"Carrier\", StringType(), True),     # \"Carrier\":\"WN\"\n",
    "  StructField(\"DayOfMonth\", IntegerType(), True), # \"DayOfMonth\":31\n",
    "  StructField(\"DayOfWeek\", IntegerType(), True),  # \"DayOfWeek\":4\n",
    "  StructField(\"DayOfYear\", IntegerType(), True),  # \"DayOfYear\":365\n",
    "  StructField(\"DepDelay\", DoubleType(), True),     # \"DepDelay\":14.0\n",
    "  StructField(\"Dest\", StringType(), True),        # \"Dest\":\"SAN\"\n",
    "  StructField(\"Distance\", DoubleType(), True),     # \"Distance\":368.0\n",
    "  StructField(\"FlightDate\", DateType(), True),    # \"FlightDate\":\"2015-12-30T16:00:00.000-08:00\"\n",
    "  StructField(\"FlightNum\", StringType(), True),   # \"FlightNum\":\"6109\"\n",
    "  StructField(\"Origin\", StringType(), True),      # \"Origin\":\"TUS\"\n",
    "])\n",
    "\n",
    "features = spark.read.json(\n",
    "  \"../data/simple_flight_delay_features.jsonl.bz2\",\n",
    "  schema=schema\n",
    ")\n",
    "features.first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Data for Nulls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Check for nulls in features before using Spark ML\n",
    "#\n",
    "null_counts = [(column, features.where(features[column].isNull()).count()) for column in features.columns]\n",
    "cols_with_nulls = filter(lambda x: x[1] > 0, null_counts)\n",
    "print(list(cols_with_nulls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add a Route Column\n",
    "\n",
    "Demonstrating the addition of a feature to our model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+------+----+-------+\n",
      "|Origin|Dest|  Route|\n",
      "+------+----+-------+\n",
      "|   ABQ| DFW|ABQ-DFW|\n",
      "|   ABQ| DFW|ABQ-DFW|\n",
      "|   ABQ| DFW|ABQ-DFW|\n",
      "|   ATL| DFW|ATL-DFW|\n",
      "|   ATL| DFW|ATL-DFW|\n",
      "+------+----+-------+\n",
      "only showing top 5 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Add a Route variable to replace FlightNum\n",
    "#\n",
    "from pyspark.sql.functions import lit, concat\n",
    "\n",
    "features_with_route = features.withColumn(\n",
    "  'Route',\n",
    "  concat(\n",
    "    features.Origin,\n",
    "    lit('-'),\n",
    "    features.Dest\n",
    "  )\n",
    ")\n",
    "features_with_route.select(\"Origin\", \"Dest\", \"Route\").show(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bucketizing ArrDelay into ArrDelayBucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+--------------+\n",
      "|ArrDelay|ArrDelayBucket|\n",
      "+--------+--------------+\n",
      "|    13.0|           2.0|\n",
      "|    17.0|           2.0|\n",
      "|    36.0|           3.0|\n",
      "|   -21.0|           0.0|\n",
      "|   -14.0|           1.0|\n",
      "|    16.0|           2.0|\n",
      "|    -7.0|           1.0|\n",
      "|    13.0|           2.0|\n",
      "|    25.0|           2.0|\n",
      "|    58.0|           3.0|\n",
      "|    14.0|           2.0|\n",
      "|     1.0|           2.0|\n",
      "|   -29.0|           0.0|\n",
      "|   -10.0|           1.0|\n",
      "|    -3.0|           1.0|\n",
      "|    -8.0|           1.0|\n",
      "|    -1.0|           1.0|\n",
      "|   -14.0|           1.0|\n",
      "|   -16.0|           0.0|\n",
      "|    18.0|           2.0|\n",
      "+--------+--------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Use pysmark.ml.feature.Bucketizer to bucketize ArrDelay\n",
    "#\n",
    "from pyspark.ml.feature import Bucketizer\n",
    "\n",
    "splits = [-float(\"inf\"), -15.0, 0, 30.0, float(\"inf\")]\n",
    "bucketizer = Bucketizer(\n",
    "  splits=splits,\n",
    "  inputCol=\"ArrDelay\",\n",
    "  outputCol=\"ArrDelayBucket\"\n",
    ")\n",
    "ml_bucketized_features = bucketizer.transform(features_with_route)\n",
    "\n",
    "# Check the buckets out\n",
    "ml_bucketized_features.select(\"ArrDelay\", \"ArrDelayBucket\").show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing Our String Fields into Numeric Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+-------------+----------------+---------------+---------------+------------+----------+-----------+\n",
      "|ArrDelay|         CRSArrTime|         CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|  Route|ArrDelayBucket|Carrier_index|DayOfMonth_index|DayOfWeek_index|DayOfYear_index|Origin_index|Dest_index|Route_index|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+-------------+----------------+---------------+---------------+------------+----------+-----------+\n",
      "|    13.0|2015-01-01 18:10:00|2015-01-01 15:30:00|     AA|         1|        4|        1|    14.0| DFW|   569.0|2014-12-31|     1024|   ABQ|ABQ-DFW|           2.0|          2.0|            25.0|            0.0|          320.0|        53.0|       2.0|      938.0|\n",
      "|    17.0|2015-01-01 10:15:00|2015-01-01 07:25:00|     AA|         1|        4|        1|    14.0| DFW|   569.0|2014-12-31|     1184|   ABQ|ABQ-DFW|           2.0|          2.0|            25.0|            0.0|          320.0|        53.0|       2.0|      938.0|\n",
      "|    36.0|2015-01-01 11:45:00|2015-01-01 09:00:00|     AA|         1|        4|        1|    -2.0| DFW|   569.0|2014-12-31|      336|   ABQ|ABQ-DFW|           3.0|          2.0|            25.0|            0.0|          320.0|        53.0|       2.0|      938.0|\n",
      "|   -21.0|2015-01-01 19:30:00|2015-01-01 17:55:00|     AA|         1|        4|        1|    -1.0| DFW|   731.0|2014-12-31|      125|   ATL|ATL-DFW|           0.0|          2.0|            25.0|            0.0|          320.0|         0.0|       2.0|       37.0|\n",
      "|   -14.0|2015-01-01 10:25:00|2015-01-01 08:55:00|     AA|         1|        4|        1|    -4.0| DFW|   731.0|2014-12-31|     1455|   ATL|ATL-DFW|           1.0|          2.0|            25.0|            0.0|          320.0|         0.0|       2.0|       37.0|\n",
      "|    16.0|2015-01-01 15:15:00|2015-01-01 13:45:00|     AA|         1|        4|        1|    15.0| DFW|   731.0|2014-12-31|     1473|   ATL|ATL-DFW|           2.0|          2.0|            25.0|            0.0|          320.0|         0.0|       2.0|       37.0|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+-------------+----------------+---------------+---------------+------------+----------+-----------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Extract features tools in with pyspark.ml.feature\n",
    "#\n",
    "from pyspark.ml.feature import StringIndexer, VectorAssembler\n",
    "\n",
    "# Turn category fields into categoric feature vectors, then drop intermediate fields\n",
    "for column in [\"Carrier\", \"DayOfMonth\", \"DayOfWeek\", \"DayOfYear\",\n",
    "               \"Origin\", \"Dest\", \"Route\"]:\n",
    "  string_indexer = StringIndexer(\n",
    "    inputCol=column,\n",
    "    outputCol=column + \"_index\"\n",
    "  )\n",
    "  ml_bucketized_features = string_indexer.fit(ml_bucketized_features)\\\n",
    "                                          .transform(ml_bucketized_features)\n",
    "\n",
    "# Check out the indexes\n",
    "ml_bucketized_features.show(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Numeric Fields into a Single Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+\n",
      "|ArrDelay|         CRSArrTime|         CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|  Route|ArrDelayBucket|        Features_vec|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+\n",
      "|    13.0|2015-01-01 18:10:00|2015-01-01 15:30:00|     AA|         1|        4|        1|    14.0| DFW|   569.0|2014-12-31|     1024|   ABQ|ABQ-DFW|           2.0|[14.0,569.0,2.0,2...|\n",
      "|    17.0|2015-01-01 10:15:00|2015-01-01 07:25:00|     AA|         1|        4|        1|    14.0| DFW|   569.0|2014-12-31|     1184|   ABQ|ABQ-DFW|           2.0|[14.0,569.0,2.0,2...|\n",
      "|    36.0|2015-01-01 11:45:00|2015-01-01 09:00:00|     AA|         1|        4|        1|    -2.0| DFW|   569.0|2014-12-31|      336|   ABQ|ABQ-DFW|           3.0|[-2.0,569.0,2.0,2...|\n",
      "|   -21.0|2015-01-01 19:30:00|2015-01-01 17:55:00|     AA|         1|        4|        1|    -1.0| DFW|   731.0|2014-12-31|      125|   ATL|ATL-DFW|           0.0|[-1.0,731.0,2.0,2...|\n",
      "|   -14.0|2015-01-01 10:25:00|2015-01-01 08:55:00|     AA|         1|        4|        1|    -4.0| DFW|   731.0|2014-12-31|     1455|   ATL|ATL-DFW|           1.0|[-4.0,731.0,2.0,2...|\n",
      "|    16.0|2015-01-01 15:15:00|2015-01-01 13:45:00|     AA|         1|        4|        1|    15.0| DFW|   731.0|2014-12-31|     1473|   ATL|ATL-DFW|           2.0|[15.0,731.0,2.0,2...|\n",
      "|    -7.0|2015-01-01 12:15:00|2015-01-01 10:45:00|     AA|         1|        4|        1|    -2.0| DFW|   731.0|2014-12-31|     1513|   ATL|ATL-DFW|           1.0|[-2.0,731.0,2.0,2...|\n",
      "|    13.0|2015-01-01 16:50:00|2015-01-01 15:25:00|     AA|         1|        4|        1|     9.0| DFW|   731.0|2014-12-31|      194|   ATL|ATL-DFW|           2.0|[9.0,731.0,2.0,25...|\n",
      "|    25.0|2015-01-01 20:30:00|2015-01-01 19:00:00|     AA|         1|        4|        1|    -2.0| DFW|   731.0|2014-12-31|      232|   ATL|ATL-DFW|           2.0|[-2.0,731.0,2.0,2...|\n",
      "|    58.0|2015-01-01 21:40:00|2015-01-01 20:15:00|     AA|         1|        4|        1|    14.0| DFW|   731.0|2014-12-31|      276|   ATL|ATL-DFW|           3.0|[14.0,731.0,2.0,2...|\n",
      "|    14.0|2015-01-01 13:25:00|2015-01-01 11:55:00|     AA|         1|        4|        1|    15.0| DFW|   731.0|2014-12-31|      314|   ATL|ATL-DFW|           2.0|[15.0,731.0,2.0,2...|\n",
      "|     1.0|2015-01-01 18:05:00|2015-01-01 16:40:00|     AA|         1|        4|        1|    -5.0| DFW|   731.0|2014-12-31|      356|   ATL|ATL-DFW|           2.0|[-5.0,731.0,2.0,2...|\n",
      "|   -29.0|2015-01-01 10:12:00|2015-01-01 08:15:00|     AA|         1|        4|        1|    -9.0| MIA|   594.0|2014-12-31|     1652|   ATL|ATL-MIA|           0.0|[-9.0,594.0,2.0,2...|\n",
      "|   -10.0|2015-01-01 08:52:00|2015-01-01 07:00:00|     AA|         1|        4|        1|    -4.0| MIA|   594.0|2014-12-31|       17|   ATL|ATL-MIA|           1.0|[-4.0,594.0,2.0,2...|\n",
      "|    -3.0|2015-01-01 23:02:00|2015-01-01 21:10:00|     AA|         1|        4|        1|    -7.0| MIA|   594.0|2014-12-31|      349|   ATL|ATL-MIA|           1.0|[-7.0,594.0,2.0,2...|\n",
      "|    -8.0|2015-01-01 14:35:00|2015-01-01 13:30:00|     AA|         1|        4|        1|    -2.0| DFW|   190.0|2014-12-31|     1023|   AUS|AUS-DFW|           1.0|[-2.0,190.0,2.0,2...|\n",
      "|    -1.0|2015-01-01 06:50:00|2015-01-01 05:50:00|     AA|         1|        4|        1|    -2.0| DFW|   190.0|2014-12-31|     1178|   AUS|AUS-DFW|           1.0|[-2.0,190.0,2.0,2...|\n",
      "|   -14.0|2015-01-01 09:40:00|2015-01-01 08:30:00|     AA|         1|        4|        1|    -6.0| DFW|   190.0|2014-12-31|     1296|   AUS|AUS-DFW|           1.0|[-6.0,190.0,2.0,2...|\n",
      "|   -16.0|2015-01-01 10:15:00|2015-01-01 09:05:00|     AA|         1|        4|        1|    -4.0| DFW|   190.0|2014-12-31|     1356|   AUS|AUS-DFW|           0.0|[-4.0,190.0,2.0,2...|\n",
      "|    18.0|2015-01-01 16:55:00|2015-01-01 15:55:00|     AA|         1|        4|        1|     3.0| DFW|   190.0|2014-12-31|     1365|   AUS|AUS-DFW|           2.0|[3.0,190.0,2.0,25...|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Handle continuous, numeric fields by combining them into one feature vector\n",
    "numeric_columns = [\"DepDelay\", \"Distance\"]\n",
    "index_columns = [\"Carrier_index\", \"DayOfMonth_index\",\n",
    "                   \"DayOfWeek_index\", \"DayOfYear_index\", \"Origin_index\",\n",
    "                   \"Origin_index\", \"Dest_index\", \"Route_index\"]\n",
    "vector_assembler = VectorAssembler(\n",
    "  inputCols=numeric_columns + index_columns,\n",
    "  outputCol=\"Features_vec\"\n",
    ")\n",
    "final_vectorized_features = vector_assembler.transform(ml_bucketized_features)\n",
    "\n",
    "# Drop the index columns\n",
    "for column in index_columns:\n",
    "  final_vectorized_features = final_vectorized_features.drop(column)\n",
    "\n",
    "# Check out the features\n",
    "final_vectorized_features.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Our Model in an Experimental Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.5967731197652838\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+--------------------+--------------------+----------+\n",
      "|ArrDelay|         CRSArrTime|         CRSDepTime|Carrier|DayOfMonth|DayOfWeek|DayOfYear|DepDelay|Dest|Distance|FlightDate|FlightNum|Origin|  Route|ArrDelayBucket|        Features_vec|       rawPrediction|         probability|prediction|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+--------------------+--------------------+----------+\n",
      "|   -10.0|2015-01-02 08:35:00|2015-01-02 07:30:00|     WN|         2|        5|        2|    -4.0| LAX|   236.0|2015-01-01|     2169|   LAS|LAS-LAX|           1.0|[-4.0,236.0,0.0,5...|[4.69368614250692...|[0.23468430712534...|       1.0|\n",
      "|   200.0|2015-01-02 14:40:00|2015-01-02 11:30:00|     B6|         2|        5|        2|   201.0| MCO|  1091.0|2015-01-01|     1887|   ORH|ORH-MCO|           3.0|[201.0,1091.0,7.0...|[0.38463403281393...|[0.01923170164069...|       3.0|\n",
      "|   -13.0|2015-01-02 15:34:00|2015-01-02 13:57:00|     EV|         2|        5|        2|    -6.0| IAD|   451.0|2015-01-01|     4757|   SDF|SDF-IAD|           1.0|[-6.0,451.0,4.0,5...|[3.99949769680097...|[0.19997488484004...|       1.0|\n",
      "|    91.0|2015-01-03 00:36:00|2015-01-02 18:58:00|     OO|         2|        5|        2|    87.0| MSP|  1535.0|2015-01-01|     5520|   LAX|LAX-MSP|           3.0|[87.0,1535.0,3.0,...|[0.24864914309886...|[0.01243245715494...|       3.0|\n",
      "|    -4.0|2015-01-03 10:30:00|2015-01-03 07:31:00|     B6|         3|        6|        3|    -1.0| PBI|  1035.0|2015-01-02|      461|   LGA|LGA-PBI|           1.0|[-1.0,1035.0,7.0,...|[5.22285454733473...|[0.26114272736673...|       1.0|\n",
      "|    43.0|2015-01-03 17:34:00|2015-01-03 14:29:00|     UA|         3|        6|        3|     2.0| FLL|  1065.0|2015-01-02|     1164|   EWR|EWR-FLL|           3.0|[2.0,1065.0,5.0,1...|[5.05268842661314...|[0.25263442133065...|       1.0|\n",
      "+--------+-------------------+-------------------+-------+----------+---------+---------+--------+----+--------+----------+---------+------+-------+--------------+--------------------+--------------------+--------------------+----------+\n",
      "only showing top 6 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# Cross validate, train and evaluate classifier\n",
    "#\n",
    "\n",
    "# Test/train split\n",
    "training_data, test_data = final_vectorized_features.randomSplit([0.7, 0.3])\n",
    "\n",
    "# Instantiate and fit random forest classifier\n",
    "from pyspark.ml.classification import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(\n",
    "  featuresCol=\"Features_vec\",\n",
    "  labelCol=\"ArrDelayBucket\",\n",
    "  maxBins=4657,\n",
    "  maxMemoryInMB=1024\n",
    ")\n",
    "model = rfc.fit(training_data)\n",
    "\n",
    "# Evaluate model using test data\n",
    "predictions = model.transform(test_data)\n",
    "\n",
    "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n",
    "evaluator = MulticlassClassificationEvaluator(labelCol=\"ArrDelayBucket\", metricName=\"accuracy\")\n",
    "accuracy = evaluator.evaluate(predictions)\n",
    "print(\"Accuracy = {}\".format(accuracy))\n",
    "\n",
    "# Check a sample\n",
    "predictions.sample(False, 0.001, 18).orderBy(\"CRSDepTime\").show(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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